{ "schema_version": 1, "measured": "2026-10-16", "timings": { "gpu": { "turbo": { "median_seconds": 2.3789115170002333, "runs_seconds": [ 2.3774779540035524, 2.3789115170002333, 2.390202248003334 ], "model_load_seconds": 1.715588327999285, "warmup_seconds": 22.368360143002064 }, "large-v3": { "median_seconds": 5.761021332997188, "runs_seconds": [ 5.761021332997188, 5.819945769995684, 5.697059067999362 ], "model_load_seconds": 3.2403821219995734, "warmup_seconds": 25.584003878000658 } }, "desktop-cpu4": { "turbo": { "median_seconds": 59.09864620099688, "runs_seconds": [ 63.415067457004625, 59.09864620099688, 58.54605773900403 ], "model_load_seconds": 2.545346104998316, "warmup_seconds": 42.94760668300296 }, "large-v3": { "median_seconds": 122.30432525800279, "runs_seconds": [ 130.08569670499855, 122.30432525800279, 120.96750149499712 ], "model_load_seconds": 5.209636588995636, "warmup_seconds": 89.83253975700063 } }, "server-cpu4": { "turbo": { "median_seconds": 83.84091449086554, "runs_seconds": [ 86.02549962094054, 83.17974668880925, 83.84091449086554 ], "model_load_seconds": 3.193624567007646, "warmup_seconds": 62.099037571810186 }, "large-v3": { "median_seconds": 179.01268508518115, "runs_seconds": [ 175.58466082904488, 180.25516751012765, 179.01268508518115 ], "model_load_seconds": 5.464853660902008, "warmup_seconds": 122.26429044292308 } } }, "demos": { "pendel": { "title": "Wie funktioniert ein Pendel?", "video": "pendel-zdf-original.mp4", "poster": "pendel-poster.jpg", "credit": "ZDF/Terra X plus/Bilderfest/ Tillman Graach/ Benjamin Herzberger/Moritz Krause/Phillip Becker, Maximilian Heß", "year": "2023", "license": "CC BY 4.0", "license_url": "https://creativecommons.org/licenses/by/4.0/legalcode.de", "source_url": "https://schule.zdf.de/video/wie-funktioniert-ein-pendel-creative-commons-clip-100", "variants": [ { "key": "turbo", "file": "pendel-gpu-turbo-baseline-aufgeteilt.vtt", "label": "Turbo · automatisch aufgeteilt", "description": "Automatisch erkannt und aufgeteilt, nicht bearbeitet. Bei 01:49 steht „Aufhängenpunkt“; gesprochen wird „Aufhängepunkt“." }, { "key": "large", "file": "pendel-gpu-large-v3-baseline-aufgeteilt.vtt", "label": "large-v3 · automatisch aufgeteilt", "description": "Automatisch erkannt und aufgeteilt, nicht bearbeitet. Schreibt bei 01:49 „Aufhängepunkt“." } ], "examples": [ { "label": "01:49 · „Aufhängepunkt“", "time": 106, "before": "turbo", "after": "large", "before_label": "Turbo abspielen", "after_label": "large-v3 abspielen" } ], "note": "Videodatei unverändert; ergänzte Untertitel unter CC BY 4.0." }, "big-data": { "title": "Big Data einfach erklärt", "video": "big-data-original.webm", "poster": "big-data-poster.jpg", "credit": "Erklaerexperten", "year": "2014", "license": "CC BY-SA 3.0", "license_url": "https://creativecommons.org/licenses/by-sa/3.0/", "source_url": "https://commons.wikimedia.org/wiki/File:Big_Data_einfach_erkl%C3%A4rt.webm", "variants": [ { "key": "turbo", "file": "big-data-gpu-turbo-baseline-pilot-inputs-v2-aufgeteilt.vtt", "label": "Turbo · automatisch aufgeteilt", "description": "Automatisch erkannt und in kurze Einblendungen aufgeteilt, nicht redaktionell bearbeitet. Beim Nachlesen fallen zum Beispiel „Jeder“ mitten im Satz (00:24) und „sie“ statt „Sie“ (00:49, wie bei large-v3) auf." }, { "key": "large", "file": "big-data-gpu-large-v3-baseline-pilot-inputs-v2-aufgeteilt.vtt", "label": "large-v3 · automatisch aufgeteilt", "description": "Automatisch erkannt und aufgeteilt, nicht bearbeitet. Enthält zwei zusätzliche Wörter (00:52 und 01:18); wie Turbo schreibt es „sie“ statt „Sie“ (00:49)." }, { "key": "large-raw", "file": "big-data-gpu-large-v3-baseline-pilot-inputs-v2.vtt", "label": "large-v3 · ohne Aufteilung", "description": "So liefert die Erkennung den Text: teils sehr lange Einblendungen. Das zusätzliche „Minuten.“ steht hier am Anfang einer Einblendung." }, { "key": "corrected", "file": "big-data-gpu-large-v3-baseline-pilot-inputs-v2-zwei-wiederholungen-korrigiert.vtt", "label": "large-v3 · zwei Wörter entfernt", "description": "Wie „large-v3 · automatisch aufgeteilt“, nur die zusätzlichen Wörter „Minuten.“ und „auf“ sind entfernt. Andere Fehler, etwa „sie“ statt „Sie“ (00:49), sind bewusst nicht korrigiert." }, { "key": "raw", "file": "big-data-gpu-turbo-baseline-pilot-inputs-v2.vtt", "label": "Turbo · ohne Aufteilung", "description": "So liefert die Erkennung den Text: Einblendungen mit bis zu 138 Zeichen." }, { "key": "reflow", "file": "big-data-gpu-turbo-baseline-pilot-inputs-v2-lesedauer-vorschlag.vtt", "label": "Turbo · Stelle 02:38 neu aufgeteilt", "description": "Um 02:38 steht ein Satzteil gemeinsam und länger im Bild, statt „wird?“ nur 0,28 Sekunden zu zeigen. Wörter unverändert, übrige Spur wie „Turbo · automatisch aufgeteilt“." } ], "examples": [ { "label": "00:52 · doppeltes „Minuten“", "time": 49.5, "before": "large-raw", "after": "corrected" }, { "label": "01:18 · doppeltes „auf“", "time": 76, "before": "large", "after": "corrected" }, { "label": "02:38 · „wird?“ steht nur kurz im Bild", "time": 151.5, "before": "turbo", "after": "reflow" } ], "note": "Film von 2014: Die damaligen Zahlen sind keine aktuellen Marktdaten. Videodatei unverändert; ergänzte Untertitel unter CC BY-SA 3.0." } }, "method": "Median aus drei Durchläufen nach Modellladen und vollständigem Pendel-Warmlauf. Erkennung einschließlich WAV-Dekodierung und VAD; ohne Upload, Download, menschliche Prüfung und Veröffentlichung.", "quality_scope": "Zwei Wortwiederholungen in Big Data (large-v3) und „Aufhängepunkt“ im Pendel-Film (Turbo schreibt „Aufhängenpunkt“) gezielt angehört und bestätigt. Beide GPU-Turbo-Spuren wurden vollständig angesehen; dabei blieb „Aufhängenpunkt“ unbemerkt, beim Nachlesen fielen weitere Schreibfehler auf (human_review.updates). Keine wortgenaue Referenz, gemessene Fehlerquote oder Prüfzeit.", "human_review": { "schema_version": 1, "date": "2026-10-16", "reviewer": "Sebastian", "kind": "internal_full_listening_review", "reported_result": "Beide vollständigen Videos mit den automatisch aufgeteilten GPU-Turbo-Spuren ohne Wortliste angesehen; keine Fehler gefunden.", "review_duration_seconds": null, "aligned_word_reference_available": false, "scope": "Subjektive vollständige Hör-/Sichtprüfung der genannten zwei Spuren am 2026-10-16. Keine unabhängige Bewertung, keine gemessene Fehlerquote und keine Aussage über andere Varianten. Nachträge vom 2026-10-20 unter updates: Die Prüfung hat einzelne Fehler übersehen; „keine Fehler gefunden“ gilt nicht als Qualitätsaussage.", "tracks": [ { "clip": "pendel", "run_id": "gpu-turbo-baseline", "repetition": 1, "result_sha256": "0b75b972ec5ccab4f6c63a9b71bf5b5aba69f97ace6db97bbd5ab17d5e8878ac", "vtt_file": "pendel-gpu-turbo-baseline-aufgeteilt.vtt", "reviewed_vtt_sha256": "81124e27ce1c94ebd3323a0e0a7d1463586cba0576c0b7f002e056de24cec0ac", "published_vtt_sha256": "d92102ba02ef90e0ef4bb77761762d67f4a2a54ff2040caffffe080d24fa1ec4", "published_changes": "Lizenzhinweis (NOTE) ergänzt; Aufteiler hält Wortteile zusammen (betrifft nur „Fitnessstudio-Besuch“ im Big-Data-Film). Wörter und Wortzeiten unverändert." }, { "clip": "big-data", "run_id": "gpu-turbo-baseline-pilot-inputs-v2", "repetition": 1, "result_sha256": "fb960c59bc983f1b33ae638eaf834a6835f1db64f04b327726dfb4dd7c009c65", "vtt_file": "big-data-gpu-turbo-baseline-pilot-inputs-v2-aufgeteilt.vtt", "reviewed_vtt_sha256": "1ea5cfb33fea663438398832961ddcccb71861b211d75d1752c03d0c292c4614", "published_vtt_sha256": "5a7dee4d0fbe3b9634b30af12ec91850d84355c9713881fabf036a2c3dbe36dc", "published_changes": "Lizenzhinweis (NOTE) ergänzt; Aufteiler hält Wortteile zusammen (betrifft nur „Fitnessstudio-Besuch“ im Big-Data-Film). Wörter und Wortzeiten unverändert." } ], "updates": [ { "date": "2026-10-20", "reviewer": "Sebastian", "kind": "targeted_listening", "clip": "pendel", "time": "01:44–01:53", "result": "Gesprochen wird „Aufhängepunkt“. Die geprüfte Turbo-Spur schreibt „Aufhängenpunkt“: ein Erkennungsfehler, der beim vollständigen Ansehen übersehen wurde." }, { "date": "2026-10-20", "kind": "text_review", "reviewer": "Gutachten Runde 1 (2026-10-18), Nachlesen im Text", "clip": "big-data", "result": "Beim Nachlesen der Turbo-Spur: „Jeder“ groß mitten in der Aufzählung (00:24), „sie“ statt der Anrede „Sie“ (00:49; schreiben beide Modelle in allen Läufen), „Fitnessstudio-Besuch“ vom eigenen Aufteiler auf zwei Einblendungen getrennt (00:28). Der Aufteiler hält Wortteile seit 2026-10-20 zusammen; Wortlaut und Wortzeiten der Erkennung bleiben unverändert." } ] }, "evidence": { "gpu-turbo-baseline-pilot-inputs-v2": { "metadata": { "model": "turbo", "model_path": "/models/huggingface/hub/models--mobiuslabsgmbh--faster-whisper-large-v3-turbo/snapshots/0a363e9161cbc7ed1431c9597a8ceaf0c4f78fcf", "device": "cuda", "compute_type": "float16", "cpu_threads": 4, "repetitions": 3, "baseline_params": { "language": "de", "task": "transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": null, "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "versions": { "faster-whisper": "1.2.1", "ctranslate2": "4.8.2", "av": "18.1.0", "onnxruntime": "1.30.0", "numpy": "2.5.3" }, "python": "3.12.3", "cpu_model": "AMD Ryzen 9 5950X 16-Core Processor", "script_sha256": "d7f158423a75d2ceae519d8b5b902dce8e2345c8201ad4f4f8e5adef94bb8082", "input_manifest_sha256": "1ad18f6b5b600661248ccf0e9b9e275a46a94f733dc5c702d0f153524503231a", "timing_scope": "Modellladen separat; warmgelaufene Erkennung einschließlich WAV-Dekodierung und VAD. Kein Upload, Download oder menschliche Korrektur.", "hardware_label": "RTX 5070 Ti 16 GB, lokaler Desktop" }, "runs": [ { "repetition": 1, "elapsed_seconds": 2.3774779540035524, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. Man kann sich daran die Finger verbrennen oder eine Zivilisation darauf begründen.", "segments": [ { "id": 1, "seek": 0, "start": 1.14, "end": 8.8, "text": " Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware", "tokens": [ 50365, 5618, 879, 32783, 5429, 11888, 312, 32338, 7129, 31126, 76, 1501, 268, 11, 978, 370, 17253, 3290, 11, 2658, 587, 2804, 2194, 2710, 260, 16985, 12, 4513, 11817, 3039, 50750 ], "avg_logprob": -0.0922803241427582, "compression_ratio": 1.4435146443514644, "no_speech_prob": 0.0, "words": [ { "start": 1.14, "end": 1.7, "word": " Der", "probability": 0.98681640625 }, { "start": 1.7, "end": 2.14, "word": " Begriff", "probability": 0.99951171875 }, { "start": 2.14, "end": 2.66, "word": " Big", "probability": 0.87255859375 }, { "start": 2.66, "end": 3.08, "word": " Data", "probability": 0.98583984375 }, { "start": 3.08, "end": 4.24, "word": " bezeichnet", "probability": 0.9990234375 }, { "start": 4.24, "end": 4.96, "word": " Datenmengen,", "probability": 0.99951171875 }, { "start": 5.06, "end": 5.44, "word": " die", "probability": 0.9990234375 }, { "start": 5.44, "end": 5.68, "word": " so", "probability": 0.99951171875 }, { "start": 5.68, "end": 6.0, "word": " groß", "probability": 0.9990234375 }, { "start": 6.0, "end": 6.3, "word": " sind,", "probability": 1.0 }, { "start": 6.54, "end": 6.66, "word": " dass", "probability": 0.9970703125 }, { "start": 6.66, "end": 6.8, "word": " man", "probability": 1.0 }, { "start": 6.8, "end": 6.94, "word": " sie", "probability": 0.99951171875 }, { "start": 6.94, "end": 7.08, "word": " mit", "probability": 1.0 }, { "start": 7.08, "end": 7.66, "word": " normaler", "probability": 0.999755859375 }, { "start": 7.66, "end": 7.94, "word": " Soft", "probability": 0.91162109375 }, { "start": 7.94, "end": 8.12, "word": "-", "probability": 0.94970703125 }, { "start": 8.12, "end": 8.28, "word": " oder", "probability": 0.9951171875 }, { "start": 8.28, "end": 8.8, "word": " Hardware", "probability": 0.99951171875 } ], "temperature": 0.0 }, { "id": 2, "seek": 0, "start": 8.8, "end": 13.64, "text": " beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann.", "tokens": [ 50750, 312, 28213, 5846, 13109, 720, 34990, 2174, 10193, 25285, 268, 1163, 31126, 331, 24024, 1063, 1979, 5417, 17897, 15510, 3213, 4028, 13, 50992 ], "avg_logprob": -0.0922803241427582, "compression_ratio": 1.4435146443514644, "no_speech_prob": 0.0, "words": [ { "start": 8.8, "end": 9.76, "word": " beziehungsweise", "probability": 0.81304931640625 }, { "start": 9.76, "end": 10.42, "word": " herkömmlichen", "probability": 0.9998779296875 }, { "start": 10.42, "end": 10.86, "word": " Methoden", "probability": 0.99951171875 }, { "start": 10.86, "end": 11.0, "word": " der", "probability": 0.99951171875 }, { "start": 11.0, "end": 12.02, "word": " Datenverarbeitung", "probability": 1.0 }, { "start": 12.02, "end": 12.62, "word": " nicht", "probability": 0.99853515625 }, { "start": 12.62, "end": 12.84, "word": " mehr", "probability": 1.0 }, { "start": 12.84, "end": 13.38, "word": " bewältigen", "probability": 0.9998372395833334 }, { "start": 13.38, "end": 13.64, "word": " kann.", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 3, "seek": 0, "start": 14.02, "end": 16.02, "text": " Wie entstehen solche Datenmengen?", "tokens": [ 51032, 9233, 35955, 2932, 29813, 31126, 76, 1501, 268, 30, 51110 ], "avg_logprob": -0.0922803241427582, "compression_ratio": 1.4435146443514644, "no_speech_prob": 0.0, "words": [ { "start": 14.02, "end": 14.6, "word": " Wie", "probability": 0.9892578125 }, { "start": 14.6, "end": 15.02, "word": " entstehen", "probability": 1.0 }, { "start": 15.02, "end": 15.32, "word": " solche", "probability": 1.0 }, { "start": 15.32, "end": 16.02, "word": " Datenmengen?", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 4, "seek": 0, "start": 16.7, "end": 24.02, "text": " Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät,", "tokens": [ 51150, 32496, 7281, 13, 4347, 5430, 768, 11106, 2804, 2194, 36538, 16053, 618, 11, 36538, 16930, 12, 36, 475, 9507, 11, 19610, 462, 278, 4488, 1028, 9219, 328, 763, 1321, 3628, 11, 51510 ], "avg_logprob": -0.0922803241427582, "compression_ratio": 1.4435146443514644, "no_speech_prob": 0.0, "words": [ { "start": 16.7, "end": 17.08, "word": " Ganz", "probability": 0.99755859375 }, { "start": 17.08, "end": 17.48, "word": " einfach.", "probability": 1.0 }, { "start": 17.84, "end": 18.28, "word": " Wir", "probability": 0.99951171875 }, { "start": 18.28, "end": 18.48, "word": " alle", "probability": 0.998046875 }, { "start": 18.48, "end": 18.94, "word": " speisen", "probability": 0.99951171875 }, { "start": 18.94, "end": 19.1, "word": " sie", "probability": 0.89013671875 }, { "start": 19.1, "end": 19.32, "word": " mit", "probability": 0.99951171875 }, { "start": 19.32, "end": 19.62, "word": " jedem", "probability": 1.0 }, { "start": 19.62, "end": 19.98, "word": " Klick,", "probability": 0.999267578125 }, { "start": 20.38, "end": 20.7, "word": " jedem", "probability": 0.99951171875 }, { "start": 20.7, "end": 21.06, "word": " Online", "probability": 0.99267578125 }, { "start": 21.06, "end": 21.58, "word": "-Einkauf,", "probability": 0.9979248046875 }, { "start": 21.8, "end": 22.28, "word": " jeder", "probability": 0.9375 }, { "start": 22.28, "end": 22.72, "word": " Eingabe", "probability": 0.9996744791666666 }, { "start": 22.72, "end": 22.88, "word": " ins", "probability": 0.99951171875 }, { "start": 22.88, "end": 24.02, "word": " Navigationsgerät,", "probability": 0.9994140625 } ], "temperature": 0.0 }, { "id": 5, "seek": 2288, "start": 24.02, "end": 32.32, "text": " Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk.", "tokens": [ 50365, 47274, 3773, 282, 2682, 25392, 5886, 313, 11, 36538, 14889, 14338, 267, 11, 36538, 45750, 28349, 1004, 12, 33, 279, 625, 11, 36538, 21387, 29685, 566, 31541, 268, 38889, 26833, 13, 50781 ], "avg_logprob": -0.09962729002357623, "compression_ratio": 1.510204081632653, "no_speech_prob": 0.0, "words": [ { "start": 24.02, "end": 24.68, "word": " Jeder", "probability": 0.75244140625 }, { "start": 24.68, "end": 25.88, "word": " Finanztransaktion,", "probability": 0.9981282552083334 }, { "start": 26.08, "end": 26.62, "word": " jedem", "probability": 0.998046875 }, { "start": 26.62, "end": 27.36, "word": " Telefonat,", "probability": 1.0 }, { "start": 27.82, "end": 28.02, "word": " jedem", "probability": 1.0 }, { "start": 28.02, "end": 28.74, "word": " Fitnessstudio", "probability": 0.99658203125 }, { "start": 28.74, "end": 29.16, "word": "-Besuch,", "probability": 0.9732666015625 }, { "start": 29.8, "end": 30.2, "word": " jedem", "probability": 0.99951171875 }, { "start": 30.2, "end": 30.48, "word": " neuen", "probability": 1.0 }, { "start": 30.48, "end": 30.9, "word": " Freund", "probability": 0.99951171875 }, { "start": 30.9, "end": 31.18, "word": " im", "probability": 1.0 }, { "start": 31.18, "end": 31.8, "word": " sozialen", "probability": 0.998779296875 }, { "start": 31.8, "end": 32.32, "word": " Netzwerk.", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 6, "seek": 2288, "start": 32.54, "end": 36.88, "text": " Das bedeutet, die Datenberge wachsen. 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"repetition": 2, "elapsed_seconds": 2.3789115170002333, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät,", "tokens": [ 51150, 32496, 7281, 13, 4347, 5430, 768, 11106, 2804, 2194, 36538, 16053, 618, 11, 36538, 16930, 12, 36, 475, 9507, 11, 19610, 462, 278, 4488, 1028, 9219, 328, 763, 1321, 3628, 11, 51510 ], "avg_logprob": -0.0922803241427582, "compression_ratio": 1.4435146443514644, "no_speech_prob": 0.0, "words": [ { "start": 16.7, "end": 17.08, "word": " Ganz", "probability": 0.99755859375 }, { "start": 17.08, "end": 17.48, "word": " einfach.", "probability": 1.0 }, { "start": 17.84, "end": 18.28, "word": " Wir", "probability": 0.99951171875 }, { "start": 18.28, "end": 18.48, "word": " alle", "probability": 0.998046875 }, { "start": 18.48, "end": 18.94, "word": " speisen", "probability": 0.99951171875 }, { "start": 18.94, "end": 19.1, "word": " sie", "probability": 0.89013671875 }, { "start": 19.1, "end": 19.32, "word": " mit", "probability": 0.99951171875 }, { "start": 19.32, "end": 19.62, "word": " jedem", "probability": 1.0 }, { "start": 19.62, "end": 19.98, "word": " Klick,", "probability": 0.999267578125 }, { "start": 20.38, "end": 20.7, "word": " jedem", "probability": 0.99951171875 }, { "start": 20.7, "end": 21.06, "word": " Online", "probability": 0.99267578125 }, { "start": 21.06, "end": 21.58, "word": "-Einkauf,", "probability": 0.9979248046875 }, { "start": 21.8, "end": 22.28, "word": " jeder", "probability": 0.9375 }, { "start": 22.28, "end": 22.72, "word": " Eingabe", "probability": 0.9996744791666666 }, { "start": 22.72, "end": 22.88, "word": " ins", "probability": 0.99951171875 }, { "start": 22.88, "end": 24.02, "word": " Navigationsgerät,", "probability": 0.9994140625 } ], "temperature": 0.0 }, { "id": 5, "seek": 2288, "start": 24.02, "end": 32.32, "text": " Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk.", "tokens": [ 50365, 47274, 3773, 282, 2682, 25392, 5886, 313, 11, 36538, 14889, 14338, 267, 11, 36538, 45750, 28349, 1004, 12, 33, 279, 625, 11, 36538, 21387, 29685, 566, 31541, 268, 38889, 26833, 13, 50781 ], "avg_logprob": -0.09962729002357623, "compression_ratio": 1.510204081632653, "no_speech_prob": 0.0, "words": [ { "start": 24.02, "end": 24.68, "word": " Jeder", "probability": 0.75244140625 }, { "start": 24.68, "end": 25.88, "word": " Finanztransaktion,", "probability": 0.9981282552083334 }, { "start": 26.08, "end": 26.62, "word": " jedem", "probability": 0.998046875 }, { "start": 26.62, "end": 27.36, "word": " Telefonat,", "probability": 1.0 }, { "start": 27.82, "end": 28.02, "word": " jedem", "probability": 1.0 }, { "start": 28.02, "end": 28.74, "word": " Fitnessstudio", "probability": 0.99658203125 }, { "start": 28.74, "end": 29.16, "word": "-Besuch,", "probability": 0.9732666015625 }, { "start": 29.8, "end": 30.2, "word": " jedem", "probability": 0.99951171875 }, { "start": 30.2, "end": 30.48, "word": " neuen", "probability": 1.0 }, { "start": 30.48, "end": 30.9, "word": " Freund", "probability": 0.99951171875 }, { "start": 30.9, "end": 31.18, "word": " im", "probability": 1.0 }, { "start": 31.18, "end": 31.8, "word": " sozialen", "probability": 0.998779296875 }, { "start": 31.8, "end": 32.32, "word": " Netzwerk.", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 6, "seek": 2288, "start": 32.54, "end": 36.88, "text": " Das bedeutet, die Datenberge wachsen. 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879, 7631, 15949, 13, 51607 ], "avg_logprob": -0.0792214892020351, "compression_ratio": 1.625, "no_speech_prob": 0.0, "words": [ { "start": 130.76, "end": 131.26, "word": " Und", "probability": 0.99951171875 }, { "start": 131.26, "end": 131.54, "word": " das", "probability": 1.0 }, { "start": 131.54, "end": 131.96, "word": " sind", "probability": 1.0 }, { "start": 131.96, "end": 132.1, "word": " nur", "probability": 1.0 }, { "start": 132.1, "end": 132.44, "word": " einige", "probability": 1.0 }, { "start": 132.44, "end": 132.96, "word": " Beispiele.", "probability": 0.9996744791666666 } ], "temperature": 0.0 }, { "id": 22, "seek": 10414, "start": 133.22, "end": 136.0, "text": " Aber Big Data wirft auch Fragen auf.", "tokens": [ 51639, 5992, 5429, 11888, 1987, 844, 2168, 25588, 2501, 13, 51761 ], "avg_logprob": -0.0792214892020351, "compression_ratio": 1.625, "no_speech_prob": 0.0, "words": [ { "start": 133.22, "end": 133.78, "word": " Aber", "probability": 0.99951171875 }, { "start": 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"repetition": 3, "elapsed_seconds": 2.390202248003334, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. Man kann sich daran die Finger verbrennen oder eine Zivilisation darauf begründen.", "segments": [ { "id": 1, "seek": 0, "start": 1.14, "end": 8.8, "text": " Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware", "tokens": [ 50365, 5618, 879, 32783, 5429, 11888, 312, 32338, 7129, 31126, 76, 1501, 268, 11, 978, 370, 17253, 3290, 11, 2658, 587, 2804, 2194, 2710, 260, 16985, 12, 4513, 11817, 3039, 50750 ], "avg_logprob": -0.0922803241427582, "compression_ratio": 1.4435146443514644, "no_speech_prob": 0.0, "words": [ { "start": 1.14, "end": 1.7, "word": " Der", "probability": 0.98681640625 }, { "start": 1.7, "end": 2.14, "word": " Begriff", "probability": 0.99951171875 }, { "start": 2.14, "end": 2.66, "word": " Big", "probability": 0.87255859375 }, { "start": 2.66, "end": 3.08, "word": " Data", "probability": 0.98583984375 }, { "start": 3.08, "end": 4.24, "word": " bezeichnet", "probability": 0.9990234375 }, { "start": 4.24, "end": 4.96, "word": " Datenmengen,", "probability": 0.99951171875 }, { "start": 5.06, "end": 5.44, "word": " die", "probability": 0.9990234375 }, { "start": 5.44, "end": 5.68, "word": " so", "probability": 0.99951171875 }, { "start": 5.68, "end": 6.0, "word": " groß", "probability": 0.9990234375 }, { "start": 6.0, "end": 6.3, "word": " sind,", "probability": 1.0 }, { "start": 6.54, "end": 6.66, "word": " dass", "probability": 0.9970703125 }, { "start": 6.66, "end": 6.8, "word": " man", "probability": 1.0 }, { "start": 6.8, "end": 6.94, "word": " sie", "probability": 0.99951171875 }, { "start": 6.94, "end": 7.08, "word": " mit", "probability": 1.0 }, { "start": 7.08, "end": 7.66, "word": " normaler", "probability": 0.999755859375 }, { "start": 7.66, "end": 7.94, "word": " Soft", "probability": 0.91162109375 }, { "start": 7.94, "end": 8.12, "word": "-", "probability": 0.94970703125 }, { "start": 8.12, "end": 8.28, "word": " oder", "probability": 0.9951171875 }, { "start": 8.28, "end": 8.8, "word": " Hardware", "probability": 0.99951171875 } ], "temperature": 0.0 }, { "id": 2, "seek": 0, "start": 8.8, "end": 13.64, "text": " beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann.", "tokens": [ 50750, 312, 28213, 5846, 13109, 720, 34990, 2174, 10193, 25285, 268, 1163, 31126, 331, 24024, 1063, 1979, 5417, 17897, 15510, 3213, 4028, 13, 50992 ], "avg_logprob": -0.0922803241427582, "compression_ratio": 1.4435146443514644, "no_speech_prob": 0.0, "words": [ { "start": 8.8, "end": 9.76, "word": " beziehungsweise", "probability": 0.81304931640625 }, { "start": 9.76, "end": 10.42, "word": " herkömmlichen", "probability": 0.9998779296875 }, { "start": 10.42, "end": 10.86, "word": " Methoden", "probability": 0.99951171875 }, { "start": 10.86, "end": 11.0, "word": " der", "probability": 0.99951171875 }, { "start": 11.0, "end": 12.02, "word": " Datenverarbeitung", "probability": 1.0 }, { "start": 12.02, "end": 12.62, "word": " nicht", "probability": 0.99853515625 }, { "start": 12.62, "end": 12.84, "word": " mehr", "probability": 1.0 }, { "start": 12.84, "end": 13.38, "word": " bewältigen", "probability": 0.9998372395833334 }, { "start": 13.38, "end": 13.64, "word": " kann.", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 3, "seek": 0, "start": 14.02, "end": 16.02, "text": " Wie entstehen solche Datenmengen?", "tokens": [ 51032, 9233, 35955, 2932, 29813, 31126, 76, 1501, 268, 30, 51110 ], "avg_logprob": -0.0922803241427582, "compression_ratio": 1.4435146443514644, "no_speech_prob": 0.0, "words": [ { "start": 14.02, "end": 14.6, "word": " Wie", "probability": 0.9892578125 }, { "start": 14.6, "end": 15.02, "word": " entstehen", "probability": 1.0 }, { "start": 15.02, "end": 15.32, "word": " solche", "probability": 1.0 }, { "start": 15.32, "end": 16.02, "word": " Datenmengen?", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 4, "seek": 0, "start": 16.7, "end": 24.02, "text": " Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät,", "tokens": [ 51150, 32496, 7281, 13, 4347, 5430, 768, 11106, 2804, 2194, 36538, 16053, 618, 11, 36538, 16930, 12, 36, 475, 9507, 11, 19610, 462, 278, 4488, 1028, 9219, 328, 763, 1321, 3628, 11, 51510 ], "avg_logprob": -0.0922803241427582, "compression_ratio": 1.4435146443514644, "no_speech_prob": 0.0, "words": [ { "start": 16.7, "end": 17.08, "word": " Ganz", "probability": 0.99755859375 }, { "start": 17.08, "end": 17.48, "word": " einfach.", "probability": 1.0 }, { "start": 17.84, "end": 18.28, "word": " Wir", "probability": 0.99951171875 }, { "start": 18.28, "end": 18.48, "word": " alle", "probability": 0.998046875 }, { "start": 18.48, "end": 18.94, "word": " speisen", "probability": 0.99951171875 }, { "start": 18.94, "end": 19.1, "word": " sie", "probability": 0.89013671875 }, { "start": 19.1, "end": 19.32, "word": " mit", "probability": 0.99951171875 }, { "start": 19.32, "end": 19.62, "word": " jedem", "probability": 1.0 }, { "start": 19.62, "end": 19.98, "word": " Klick,", "probability": 0.999267578125 }, { "start": 20.38, "end": 20.7, "word": " jedem", "probability": 0.99951171875 }, { "start": 20.7, "end": 21.06, "word": " Online", "probability": 0.99267578125 }, { "start": 21.06, "end": 21.58, "word": "-Einkauf,", "probability": 0.9979248046875 }, { "start": 21.8, "end": 22.28, "word": " jeder", "probability": 0.9375 }, { "start": 22.28, "end": 22.72, "word": " Eingabe", "probability": 0.9996744791666666 }, { "start": 22.72, "end": 22.88, "word": " ins", "probability": 0.99951171875 }, { "start": 22.88, "end": 24.02, "word": " Navigationsgerät,", "probability": 0.9994140625 } ], "temperature": 0.0 }, { "id": 5, "seek": 2288, "start": 24.02, "end": 32.32, "text": " Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk.", "tokens": [ 50365, 47274, 3773, 282, 2682, 25392, 5886, 313, 11, 36538, 14889, 14338, 267, 11, 36538, 45750, 28349, 1004, 12, 33, 279, 625, 11, 36538, 21387, 29685, 566, 31541, 268, 38889, 26833, 13, 50781 ], "avg_logprob": -0.09962729002357623, "compression_ratio": 1.510204081632653, "no_speech_prob": 0.0, "words": [ { "start": 24.02, "end": 24.68, "word": " Jeder", "probability": 0.75244140625 }, { "start": 24.68, "end": 25.88, "word": " Finanztransaktion,", "probability": 0.9981282552083334 }, { "start": 26.08, "end": 26.62, "word": " jedem", "probability": 0.998046875 }, { "start": 26.62, "end": 27.36, "word": " Telefonat,", "probability": 1.0 }, { "start": 27.82, "end": 28.02, "word": " jedem", "probability": 1.0 }, { "start": 28.02, "end": 28.74, "word": " Fitnessstudio", "probability": 0.99658203125 }, { "start": 28.74, "end": 29.16, "word": "-Besuch,", "probability": 0.9732666015625 }, { "start": 29.8, "end": 30.2, "word": " jedem", "probability": 0.99951171875 }, { "start": 30.2, "end": 30.48, "word": " neuen", "probability": 1.0 }, { "start": 30.48, "end": 30.9, "word": " Freund", "probability": 0.99951171875 }, { "start": 30.9, "end": 31.18, "word": " im", "probability": 1.0 }, { "start": 31.18, "end": 31.8, "word": " sozialen", "probability": 0.998779296875 }, { "start": 31.8, "end": 32.32, "word": " Netzwerk.", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 6, "seek": 2288, "start": 32.54, "end": 36.88, "text": " Das bedeutet, die Datenberge wachsen. Und zwar rasant.", "tokens": [ 50814, 2846, 27018, 11, 978, 31126, 607, 432, 261, 31625, 13, 2719, 19054, 367, 10633, 13, 51010 ], "avg_logprob": -0.09962729002357623, "compression_ratio": 1.510204081632653, "no_speech_prob": 0.0, "words": [ { "start": 32.54, "end": 33.18, "word": " Das", "probability": 1.0 }, { "start": 33.18, "end": 33.66, "word": " bedeutet,", "probability": 1.0 }, { "start": 34.12, "end": 34.5, "word": " die", "probability": 1.0 }, { "start": 34.5, "end": 35.2, "word": " Datenberge", "probability": 0.9991861979166666 }, { "start": 35.2, "end": 35.58, "word": " wachsen.", "probability": 1.0 }, { "start": 35.76, "end": 36.22, "word": " Und", "probability": 1.0 }, { "start": 36.22, "end": 36.44, "word": " zwar", "probability": 1.0 }, { "start": 36.44, "end": 36.88, "word": " rasant.", "probability": 0.999755859375 } ], "temperature": 0.0 }, { "id": 7, "seek": 2288, "start": 38.69, "end": 44.75, "text": " Die gleiche Menge an Daten, die die Menschheit von ihrem 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"start": 122.06, "end": 130.42, "text": " Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden.", "tokens": [ 51081, 27577, 6310, 5429, 11888, 36905, 11, 1105, 14280, 38192, 18021, 36272, 11368, 425, 25657, 674, 11793, 18844, 11, 43932, 46820, 5084, 674, 14255, 36776, 2501, 2804, 2164, 16236, 339, 716, 4380, 13, 51490 ], "avg_logprob": -0.08915844187140465, "compression_ratio": 1.625, "no_speech_prob": 0.00490570068359375, "words": [ { "start": 122.06, "end": 122.7, "word": " Unternehmen", "probability": 0.99951171875 }, { "start": 122.7, "end": 123.08, "word": " können", "probability": 1.0 }, { "start": 123.08, "end": 123.34, "word": " Big", "probability": 0.99609375 }, { "start": 123.34, "end": 123.56, "word": " Data", "probability": 0.9892578125 }, { "start": 123.56, "end": 123.84, "word": " nutzen,", "probability": 0.9990234375 }, { "start": 124.16, "end": 124.6, "word": " um", "probability": 1.0 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0.9998046875 } ], "temperature": 0.0 }, { "id": 22, "seek": 10406, "start": 130.64, "end": 132.88, "text": " Und das sind nur einige Beispiele.", "tokens": [ 51519, 2719, 1482, 3290, 4343, 28338, 879, 7631, 15949, 13, 51611 ], "avg_logprob": -0.08915844187140465, "compression_ratio": 1.625, "no_speech_prob": 0.00490570068359375, "words": [ { "start": 130.64, "end": 131.18, "word": " Und", "probability": 0.99853515625 }, { "start": 131.18, "end": 131.4, "word": " das", "probability": 1.0 }, { "start": 131.4, "end": 131.88, "word": " sind", "probability": 1.0 }, { "start": 131.88, "end": 132.04, "word": " nur", "probability": 1.0 }, { "start": 132.04, "end": 132.3, "word": " einige", "probability": 0.99951171875 }, { "start": 132.3, "end": 132.88, "word": " Beispiele.", "probability": 0.9998372395833334 } ], "temperature": 0.0 }, { "id": 23, "seek": 10406, "start": 133.36, "end": 135.86, "text": " Aber Big Data wirft auch Fragen auf.", "tokens": [ 51643, 5992, 5429, 11888, 1987, 844, 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"5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "repetition": 2, "elapsed_seconds": 5.819945769995684, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware- beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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0.9998046875 } ], "temperature": 0.0 }, { "id": 22, "seek": 10406, "start": 130.64, "end": 132.88, "text": " Und das sind nur einige Beispiele.", "tokens": [ 51519, 2719, 1482, 3290, 4343, 28338, 879, 7631, 15949, 13, 51611 ], "avg_logprob": -0.08915844187140465, "compression_ratio": 1.625, "no_speech_prob": 0.00490570068359375, "words": [ { "start": 130.64, "end": 131.18, "word": " Und", "probability": 0.99853515625 }, { "start": 131.18, "end": 131.4, "word": " das", "probability": 1.0 }, { "start": 131.4, "end": 131.88, "word": " sind", "probability": 1.0 }, { "start": 131.88, "end": 132.04, "word": " nur", "probability": 1.0 }, { "start": 132.04, "end": 132.3, "word": " einige", "probability": 0.99951171875 }, { "start": 132.3, "end": 132.88, "word": " Beispiele.", "probability": 0.9998372395833334 } ], "temperature": 0.0 }, { "id": 23, "seek": 10406, "start": 133.36, "end": 135.86, "text": " Aber Big Data wirft auch Fragen auf.", "tokens": [ 51643, 5992, 5429, 11888, 1987, 844, 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"5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "repetition": 3, "elapsed_seconds": 5.697059067999362, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware- beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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Kein Upload, Download oder menschliche Korrektur.", "hardware_label": "Ryzen 9 5950X, Desktop, Docker-Limit 4 CPUs / 8 GiB, 4 Threads; KEIN Mietserver" }, "runs": [ { "repetition": 1, "elapsed_seconds": 63.415067457004625, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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Daten?", "tokens": [ 50414, 343, 443, 13218, 26377, 978, 11, 47543, 31228, 10193, 11, 31126, 30, 50527 ], "avg_logprob": -0.1166653387379228, "compression_ratio": 1.4487632508833923, "no_speech_prob": 6.0476185691216156e-12, "words": [ { "start": 136.85, "end": 137.18, "word": " Wem", "probability": 0.9751220345497131 }, { "start": 137.18, "end": 137.52, "word": " gehören", "probability": 0.999862402677536 }, { "start": 137.52, "end": 137.74, "word": " die,", "probability": 0.9997397065162659 }, { "start": 137.74, "end": 138.14, "word": " häufig", "probability": 0.9997976422309875 }, { "start": 138.14, "end": 138.86, "word": " persönlichen,", "probability": 0.9985114634037018 }, { "start": 138.96, "end": 139.22, "word": " Daten?", "probability": 0.9998205304145813 } ], "temperature": 0.0 }, { "id": 24, "seek": 13202, "start": 139.48, "end": 143.62, "text": " Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren?", "tokens": [ 50564, 12810, 785, 41484, 1739, 11, 4797, 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"condition_on_previous_text": false, "initial_prompt": null, "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "repetition": 2, "elapsed_seconds": 59.09864620099688, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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Daten?", "tokens": [ 50414, 343, 443, 13218, 26377, 978, 11, 47543, 31228, 10193, 11, 31126, 30, 50527 ], "avg_logprob": -0.1166653387379228, "compression_ratio": 1.4487632508833923, "no_speech_prob": 6.0476185691216156e-12, "words": [ { "start": 136.85, "end": 137.18, "word": " Wem", "probability": 0.9751220345497131 }, { "start": 137.18, "end": 137.52, "word": " gehören", "probability": 0.999862402677536 }, { "start": 137.52, "end": 137.74, "word": " die,", "probability": 0.9997397065162659 }, { "start": 137.74, "end": 138.14, "word": " häufig", "probability": 0.9997976422309875 }, { "start": 138.14, "end": 138.86, "word": " persönlichen,", "probability": 0.9985114634037018 }, { "start": 138.96, "end": 139.22, "word": " Daten?", "probability": 0.9998205304145813 } ], "temperature": 0.0 }, { "id": 24, "seek": 13202, "start": 139.48, "end": 143.62, "text": " Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren?", "tokens": [ 50564, 12810, 785, 41484, 1739, 11, 4797, 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"condition_on_previous_text": false, "initial_prompt": null, "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "repetition": 3, "elapsed_seconds": 58.54605773900403, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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"end": 167.94, "text": " Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen.", "tokens": [ 50393, 2313, 1418, 1343, 20911, 3355, 4643, 1163, 3951, 1479, 547, 1063, 730, 3697, 84, 433, 7131, 978, 29453, 13712, 45743, 2470, 13, 50568 ], "avg_logprob": -0.09222627215915256, "compression_ratio": 1.2635658914728682, "no_speech_prob": 0.004435638897120953, "words": [ { "start": 164.24, "end": 164.5, "word": " Es", "probability": 0.9595826268196106 }, { "start": 164.5, "end": 164.7, "word": " ist", "probability": 0.9992766976356506 }, { "start": 164.7, "end": 164.82, "word": " ein", "probability": 0.9963239431381226 }, { "start": 164.82, "end": 165.14, "word": " wenig", "probability": 0.9994833469390869 }, { "start": 165.14, "end": 165.46, "word": " wie", "probability": 0.9864279627799988 }, { "start": 165.46, "end": 165.62, "word": " bei", "probability": 0.9993823766708374 }, { "start": 165.62, "end": 165.78, "word": " der", "probability": 0.999256432056427 }, { 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172.62, "word": " begründen.", "probability": 0.9997246265411377 } ], "temperature": 0.0 } ], "params": { "language": "de", "task": "transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": null, "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "repetition": 2, "elapsed_seconds": 122.30432525800279, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware- beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. Man kann sich daran die Finger verbrennen oder eine Zivilisation darauf begründen.", "segments": [ { "id": 1, "seek": 0, "start": 1.14, "end": 8.82, "text": " Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware-", "tokens": [ 50365, 5618, 879, 32783, 5429, 11888, 312, 32338, 7129, 31126, 76, 1501, 268, 11, 978, 370, 17253, 3290, 11, 2658, 587, 2804, 2194, 2710, 260, 16985, 12, 4513, 11817, 3039, 12, 50763 ], "avg_logprob": -0.06260484566583353, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00022367834753822535, "words": [ { "start": 1.14, "end": 1.64, "word": " Der", "probability": 0.9884137511253357 }, { "start": 1.64, "end": 2.1, "word": " Begriff", "probability": 0.9970188140869141 }, { "start": 2.1, "end": 2.58, "word": " Big", "probability": 0.9010357856750488 }, { "start": 2.58, "end": 2.88, "word": " Data", "probability": 0.996129035949707 }, { "start": 2.88, "end": 4.2, "word": " bezeichnet", "probability": 0.9988680283228556 }, { "start": 4.2, "end": 4.88, "word": " Datenmengen,", "probability": 0.9997534602880478 }, { "start": 4.92, "end": 5.38, "word": " die", "probability": 0.9991945624351501 }, { "start": 5.38, "end": 5.56, "word": " so", "probability": 0.9996321201324463 }, { "start": 5.56, "end": 5.88, "word": " groß", "probability": 0.992250382900238 }, { "start": 5.88, "end": 6.16, "word": " sind,", "probability": 0.9999171495437622 }, { "start": 6.28, "end": 6.6, "word": " dass", "probability": 0.9983090162277222 }, { "start": 6.6, "end": 6.74, "word": " man", "probability": 0.9996789693832397 }, { "start": 6.74, "end": 6.86, "word": " sie", "probability": 0.9991834759712219 }, { "start": 6.86, "end": 7.02, "word": " mit", "probability": 0.9996137022972107 }, { "start": 7.02, "end": 7.56, "word": " normaler", "probability": 0.9997826218605042 }, { "start": 7.56, "end": 7.8, "word": " Soft", "probability": 0.8730633854866028 }, { "start": 7.8, "end": 7.98, "word": "-", "probability": 0.9771453142166138 }, { "start": 7.98, "end": 8.2, "word": " oder", "probability": 0.9976436495780945 }, { "start": 8.2, "end": 8.68, "word": " Hardware", "probability": 0.999077707529068 }, { "start": 8.68, "end": 8.82, "word": "-", "probability": 0.0921614021062851 } ], "temperature": 0.0 }, { "id": 2, "seek": 0, "start": 8.82, "end": 13.52, "text": " beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann.", "tokens": [ 50763, 312, 28213, 5846, 13109, 720, 34990, 2174, 10193, 25285, 268, 1163, 31126, 331, 24024, 1063, 1979, 5417, 17897, 15510, 3213, 4028, 13, 50991 ], "avg_logprob": -0.06260484566583353, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00022367834753822535, "words": [ { "start": 8.82, "end": 9.68, "word": " beziehungsweise", "probability": 0.8435501307249069 }, { "start": 9.68, "end": 10.3, "word": " herkömmlichen", "probability": 0.9991878718137741 }, { "start": 10.3, "end": 10.84, "word": " Methoden", "probability": 0.9992599487304688 }, { "start": 10.84, "end": 10.94, "word": " der", "probability": 0.9995343685150146 }, { "start": 10.94, "end": 11.96, "word": " Datenverarbeitung", "probability": 0.9997771084308624 }, { "start": 11.96, "end": 12.48, "word": " nicht", "probability": 0.9928142428398132 }, { "start": 12.48, "end": 12.78, "word": " mehr", "probability": 0.9997183680534363 }, { "start": 12.78, "end": 13.26, "word": " bewältigen", "probability": 0.9998686114947001 }, { "start": 13.26, "end": 13.52, "word": " kann.", "probability": 0.9998688697814941 } ], "temperature": 0.0 }, { "id": 3, "seek": 0, "start": 13.86, "end": 15.98, "text": " Wie entstehen solche Datenmengen?", "tokens": [ 51023, 9233, 35955, 2932, 29813, 31126, 76, 1501, 268, 30, 51110 ], "avg_logprob": -0.06260484566583353, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00022367834753822535, "words": [ { "start": 13.86, "end": 14.5, "word": " Wie", "probability": 0.9879712462425232 }, { "start": 14.5, "end": 14.96, "word": " entstehen", "probability": 0.9997982978820801 }, { "start": 14.96, "end": 15.24, "word": " solche", "probability": 0.9980485439300537 }, { "start": 15.24, "end": 15.98, "word": " Datenmengen?", "probability": 0.9998074769973755 } ], "temperature": 0.0 }, { "id": 4, "seek": 0, "start": 16.36, "end": 24.02, "text": " Ganz einfach. 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"end": 167.94, "text": " Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen.", "tokens": [ 50393, 2313, 1418, 1343, 20911, 3355, 4643, 1163, 3951, 1479, 547, 1063, 730, 3697, 84, 433, 7131, 978, 29453, 13712, 45743, 2470, 13, 50568 ], "avg_logprob": -0.09222627215915256, "compression_ratio": 1.2635658914728682, "no_speech_prob": 0.004435638897120953, "words": [ { "start": 164.24, "end": 164.5, "word": " Es", "probability": 0.9595826268196106 }, { "start": 164.5, "end": 164.7, "word": " ist", "probability": 0.9992766976356506 }, { "start": 164.7, "end": 164.82, "word": " ein", "probability": 0.9963239431381226 }, { "start": 164.82, "end": 165.14, "word": " wenig", "probability": 0.9994833469390869 }, { "start": 165.14, "end": 165.46, "word": " wie", "probability": 0.9864279627799988 }, { "start": 165.46, "end": 165.62, "word": " bei", "probability": 0.9993823766708374 }, { "start": 165.62, "end": 165.78, "word": " der", "probability": 0.999256432056427 }, { 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172.62, "word": " begründen.", "probability": 0.9997246265411377 } ], "temperature": 0.0 } ], "params": { "language": "de", "task": "transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": null, "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "repetition": 3, "elapsed_seconds": 120.96750149499712, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware- beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. Man kann sich daran die Finger verbrennen oder eine Zivilisation darauf begründen.", "segments": [ { "id": 1, "seek": 0, "start": 1.14, "end": 8.82, "text": " Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware-", "tokens": [ 50365, 5618, 879, 32783, 5429, 11888, 312, 32338, 7129, 31126, 76, 1501, 268, 11, 978, 370, 17253, 3290, 11, 2658, 587, 2804, 2194, 2710, 260, 16985, 12, 4513, 11817, 3039, 12, 50763 ], "avg_logprob": -0.06260484566583353, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00022367834753822535, "words": [ { "start": 1.14, "end": 1.64, "word": " Der", "probability": 0.9884137511253357 }, { "start": 1.64, "end": 2.1, "word": " Begriff", "probability": 0.9970188140869141 }, { "start": 2.1, "end": 2.58, "word": " Big", "probability": 0.9010357856750488 }, { "start": 2.58, "end": 2.88, "word": " Data", "probability": 0.996129035949707 }, { "start": 2.88, "end": 4.2, "word": " bezeichnet", "probability": 0.9988680283228556 }, { "start": 4.2, "end": 4.88, "word": " Datenmengen,", "probability": 0.9997534602880478 }, { "start": 4.92, "end": 5.38, "word": " die", "probability": 0.9991945624351501 }, { "start": 5.38, "end": 5.56, "word": " so", "probability": 0.9996321201324463 }, { "start": 5.56, "end": 5.88, "word": " groß", "probability": 0.992250382900238 }, { "start": 5.88, "end": 6.16, "word": " sind,", "probability": 0.9999171495437622 }, { "start": 6.28, "end": 6.6, "word": " dass", "probability": 0.9983090162277222 }, { "start": 6.6, "end": 6.74, "word": " man", "probability": 0.9996789693832397 }, { "start": 6.74, "end": 6.86, "word": " sie", "probability": 0.9991834759712219 }, { "start": 6.86, "end": 7.02, "word": " mit", "probability": 0.9996137022972107 }, { "start": 7.02, "end": 7.56, "word": " normaler", "probability": 0.9997826218605042 }, { "start": 7.56, "end": 7.8, "word": " Soft", "probability": 0.8730633854866028 }, { "start": 7.8, "end": 7.98, "word": "-", "probability": 0.9771453142166138 }, { "start": 7.98, "end": 8.2, "word": " oder", "probability": 0.9976436495780945 }, { "start": 8.2, "end": 8.68, "word": " Hardware", "probability": 0.999077707529068 }, { "start": 8.68, "end": 8.82, "word": "-", "probability": 0.0921614021062851 } ], "temperature": 0.0 }, { "id": 2, "seek": 0, "start": 8.82, "end": 13.52, "text": " beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann.", "tokens": [ 50763, 312, 28213, 5846, 13109, 720, 34990, 2174, 10193, 25285, 268, 1163, 31126, 331, 24024, 1063, 1979, 5417, 17897, 15510, 3213, 4028, 13, 50991 ], "avg_logprob": -0.06260484566583353, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00022367834753822535, "words": [ { "start": 8.82, "end": 9.68, "word": " beziehungsweise", "probability": 0.8435501307249069 }, { "start": 9.68, "end": 10.3, "word": " herkömmlichen", "probability": 0.9991878718137741 }, { "start": 10.3, "end": 10.84, "word": " Methoden", "probability": 0.9992599487304688 }, { "start": 10.84, "end": 10.94, "word": " der", "probability": 0.9995343685150146 }, { "start": 10.94, "end": 11.96, "word": " Datenverarbeitung", "probability": 0.9997771084308624 }, { "start": 11.96, "end": 12.48, "word": " nicht", "probability": 0.9928142428398132 }, { "start": 12.48, "end": 12.78, "word": " mehr", "probability": 0.9997183680534363 }, { "start": 12.78, "end": 13.26, "word": " bewältigen", "probability": 0.9998686114947001 }, { "start": 13.26, "end": 13.52, "word": " kann.", "probability": 0.9998688697814941 } ], "temperature": 0.0 }, { "id": 3, "seek": 0, "start": 13.86, "end": 15.98, "text": " Wie entstehen solche Datenmengen?", "tokens": [ 51023, 9233, 35955, 2932, 29813, 31126, 76, 1501, 268, 30, 51110 ], "avg_logprob": -0.06260484566583353, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00022367834753822535, "words": [ { "start": 13.86, "end": 14.5, "word": " Wie", "probability": 0.9879712462425232 }, { "start": 14.5, "end": 14.96, "word": " entstehen", "probability": 0.9997982978820801 }, { "start": 14.96, "end": 15.24, "word": " solche", "probability": 0.9980485439300537 }, { "start": 15.24, "end": 15.98, "word": " Datenmengen?", "probability": 0.9998074769973755 } ], "temperature": 0.0 }, { "id": 4, "seek": 0, "start": 16.36, "end": 24.02, "text": " Ganz einfach. 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Kein Upload, Download oder menschliche Korrektur.", "hardware_label": "Hetzner CX43, AMD EPYC-Rome shared vCPU; Container 4 CPUs / 8 GiB, 4 Threads; bestehender Server mit Hintergrunddiensten" }, "runs": [ { "repetition": 1, "elapsed_seconds": 86.02549962094054, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick. Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, Jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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kontrollieren?", "probability": 0.999940812587738 } ], "temperature": 0.0 }, { "id": 26, "seek": 13202, "start": 144.06, "end": 146.58, "text": " Wollen wir, und das ist ein reales Beispiel,", "tokens": [ 50786, 343, 26669, 1987, 11, 674, 1482, 1418, 1343, 957, 279, 13772, 11, 50897 ], "avg_logprob": -0.1152805500386054, "compression_ratio": 1.4487632508833923, "no_speech_prob": 5.6068483189619656e-12, "words": [ { "start": 144.06, "end": 144.7, "word": " Wollen", "probability": 0.999454915523529 }, { "start": 144.7, "end": 144.98, "word": " wir,", "probability": 0.9999586343765259 }, { "start": 145.12, "end": 145.36, "word": " und", "probability": 0.9988279938697815 }, { "start": 145.36, "end": 145.52, "word": " das", "probability": 0.9999644756317139 }, { "start": 145.52, "end": 145.66, "word": " ist", "probability": 0.9999338388442993 }, { "start": 145.66, "end": 145.8, "word": " ein", "probability": 0.9999268054962158 }, { "start": 145.8, "end": 146.22, "word": " reales", 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auch menschliches Verhalten voraussagbar wird?", "tokens": [ 51202, 2719, 10047, 34313, 1163, 9394, 31744, 11, 364, 1371, 2168, 10923, 339, 45502, 4281, 15022, 371, 3252, 2023, 559, 5356, 4578, 30, 51473 ], "avg_logprob": -0.1152805500386054, "compression_ratio": 1.4487632508833923, "no_speech_prob": 5.6068483189619656e-12, "words": [ { "start": 152.74, "end": 153.38, "word": " Und", "probability": 0.9996485710144043 }, { "start": 153.38, "end": 153.66, "word": " kommt", "probability": 0.9997747540473938 }, { "start": 153.66, "end": 154.14, "word": " irgendwann", "probability": 0.9999011754989624 }, { "start": 154.14, "end": 154.32, "word": " der", "probability": 0.9999699592590332 }, { "start": 154.32, "end": 154.84, "word": " Zeitpunkt,", "probability": 0.9997773170471191 }, { "start": 155.06, "end": 155.4, "word": " an", "probability": 0.9958078861236572 }, { "start": 155.4, "end": 155.5, "word": " dem", "probability": 0.999789297580719 }, { "start": 155.5, "end": 155.72, "word": " 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" daran", "probability": 0.9999030828475952 }, { "start": 169.48, "end": 169.64, "word": " die", "probability": 0.9999769926071167 }, { "start": 169.64, "end": 169.88, "word": " Finger", "probability": 0.9999593496322632 }, { "start": 169.88, "end": 170.4, "word": " verbrennen", "probability": 0.9997816880544027 }, { "start": 170.4, "end": 170.88, "word": " oder", "probability": 0.9995936751365662 }, { "start": 170.88, "end": 171.18, "word": " eine", "probability": 0.9996598958969116 }, { "start": 171.18, "end": 171.92, "word": " Zivilisation", "probability": 0.9998501141866049 }, { "start": 171.92, "end": 172.2, "word": " darauf", "probability": 0.9999281167984009 }, { "start": 172.2, "end": 172.7, "word": " begründen.", "probability": 0.9994250535964966 } ], "temperature": 0.0 } ], "params": { "language": "de", "task": "transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": null, "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "repetition": 2, "elapsed_seconds": 83.17974668880925, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick. Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, Jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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kontrollieren?", "probability": 0.999940812587738 } ], "temperature": 0.0 }, { "id": 26, "seek": 13202, "start": 144.06, "end": 146.58, "text": " Wollen wir, und das ist ein reales Beispiel,", "tokens": [ 50786, 343, 26669, 1987, 11, 674, 1482, 1418, 1343, 957, 279, 13772, 11, 50897 ], "avg_logprob": -0.1152805500386054, "compression_ratio": 1.4487632508833923, "no_speech_prob": 5.6068483189619656e-12, "words": [ { "start": 144.06, "end": 144.7, "word": " Wollen", "probability": 0.999454915523529 }, { "start": 144.7, "end": 144.98, "word": " wir,", "probability": 0.9999586343765259 }, { "start": 145.12, "end": 145.36, "word": " und", "probability": 0.9988279938697815 }, { "start": 145.36, "end": 145.52, "word": " das", "probability": 0.9999644756317139 }, { "start": 145.52, "end": 145.66, "word": " ist", "probability": 0.9999338388442993 }, { "start": 145.66, "end": 145.8, "word": " ein", "probability": 0.9999268054962158 }, { "start": 145.8, "end": 146.22, "word": " reales", 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" daran", "probability": 0.9999030828475952 }, { "start": 169.48, "end": 169.64, "word": " die", "probability": 0.9999769926071167 }, { "start": 169.64, "end": 169.88, "word": " Finger", "probability": 0.9999593496322632 }, { "start": 169.88, "end": 170.4, "word": " verbrennen", "probability": 0.9997816880544027 }, { "start": 170.4, "end": 170.88, "word": " oder", "probability": 0.9995936751365662 }, { "start": 170.88, "end": 171.18, "word": " eine", "probability": 0.9996598958969116 }, { "start": 171.18, "end": 171.92, "word": " Zivilisation", "probability": 0.9998501141866049 }, { "start": 171.92, "end": 172.2, "word": " darauf", "probability": 0.9999281167984009 }, { "start": 172.2, "end": 172.7, "word": " begründen.", "probability": 0.9994250535964966 } ], "temperature": 0.0 } ], "params": { "language": "de", "task": "transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": null, "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "repetition": 3, "elapsed_seconds": 83.84091449086554, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick. Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, Jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Kräufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät,", "tokens": [ 51150, 32496, 7281, 13, 4347, 5430, 768, 11106, 2804, 2194, 36538, 16053, 618, 11, 36538, 16930, 12, 36, 475, 9507, 11, 19610, 462, 278, 4488, 1028, 9219, 328, 763, 1321, 3628, 11, 51510 ], "avg_logprob": -0.0620685430864493, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00020198698621243238, "words": [ { "start": 16.36, "end": 17.0, "word": " Ganz", "probability": 0.998264491558075 }, { "start": 17.0, "end": 17.36, "word": " einfach.", "probability": 0.9997331500053406 }, { "start": 17.6, "end": 18.18, "word": " Wir", "probability": 0.9990766048431396 }, { "start": 18.18, "end": 18.38, "word": " alle", "probability": 0.994965136051178 }, { "start": 18.38, "end": 18.88, "word": " speisen", "probability": 0.9991296529769897 }, { "start": 18.88, "end": 19.04, "word": " sie", "probability": 0.9970335960388184 }, { "start": 19.04, "end": 19.22, "word": " mit", 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begründen.", "probability": 0.9995790123939514 } ], "temperature": 0.0 } ], "params": { "language": "de", "task": "transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": null, "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "repetition": 2, "elapsed_seconds": 180.25516751012765, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware- beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Kräufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. Man kann sich daran die Finger verbrennen oder eine Zivilisation darauf begründen.", "segments": [ { "id": 1, "seek": 0, "start": 1.14, "end": 8.82, "text": " Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware-", "tokens": [ 50365, 5618, 879, 32783, 5429, 11888, 312, 32338, 7129, 31126, 76, 1501, 268, 11, 978, 370, 17253, 3290, 11, 2658, 587, 2804, 2194, 2710, 260, 16985, 12, 4513, 11817, 3039, 12, 50763 ], "avg_logprob": -0.0620685430864493, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00020198698621243238, "words": [ { "start": 1.14, "end": 1.64, "word": " Der", "probability": 0.9884618520736694 }, { "start": 1.64, "end": 2.1, "word": " Begriff", "probability": 0.9974938333034515 }, { "start": 2.1, "end": 2.58, "word": " Big", "probability": 0.9010727405548096 }, { "start": 2.58, "end": 2.88, "word": " Data", "probability": 0.9963210821151733 }, { "start": 2.88, "end": 4.2, "word": " bezeichnet", "probability": 0.9987662037213644 }, { "start": 4.2, "end": 4.88, "word": " Datenmengen,", "probability": 0.9997581094503403 }, { "start": 4.92, "end": 5.38, "word": " die", "probability": 0.9991471767425537 }, { "start": 5.38, "end": 5.56, "word": " so", "probability": 0.9996447563171387 }, { "start": 5.56, "end": 5.86, "word": " groß", "probability": 0.9921636581420898 }, { "start": 5.86, "end": 6.16, "word": " sind,", "probability": 0.9999066591262817 }, { "start": 6.28, "end": 6.6, "word": " dass", "probability": 0.9982548356056213 }, { "start": 6.6, "end": 6.74, "word": " man", "probability": 0.9997039437294006 }, { "start": 6.74, "end": 6.86, "word": " sie", "probability": 0.9991369843482971 }, { "start": 6.86, "end": 7.02, "word": " mit", "probability": 0.9995371103286743 }, { "start": 7.02, "end": 7.56, "word": " normaler", "probability": 0.9997589588165283 }, { "start": 7.56, "end": 7.8, "word": " Soft", "probability": 0.8935483694076538 }, { "start": 7.8, "end": 7.98, "word": "-", "probability": 0.9729250073432922 }, { "start": 7.98, "end": 8.2, "word": " oder", "probability": 0.9974942207336426 }, { "start": 8.2, "end": 8.68, "word": " Hardware", "probability": 0.9988400936126709 }, { "start": 8.68, "end": 8.82, "word": "-", "probability": 0.12491188198328018 } ], "temperature": 0.0 }, { "id": 2, "seek": 0, "start": 8.82, "end": 13.52, "text": " beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann.", "tokens": [ 50763, 312, 28213, 5846, 13109, 720, 34990, 2174, 10193, 25285, 268, 1163, 31126, 331, 24024, 1063, 1979, 5417, 17897, 15510, 3213, 4028, 13, 50991 ], "avg_logprob": -0.0620685430864493, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00020198698621243238, "words": [ { "start": 8.82, "end": 9.68, "word": " beziehungsweise", "probability": 0.8407757058739662 }, { "start": 9.68, "end": 10.3, "word": " herkömmlichen", "probability": 0.999100923538208 }, { "start": 10.3, "end": 10.84, "word": " Methoden", "probability": 0.9991872608661652 }, { "start": 10.84, "end": 10.94, "word": " der", "probability": 0.9995253086090088 }, { "start": 10.94, "end": 11.96, "word": " Datenverarbeitung", "probability": 0.9997965097427368 }, { "start": 11.96, "end": 12.48, "word": " nicht", "probability": 0.9916003942489624 }, { "start": 12.48, "end": 12.78, "word": " mehr", "probability": 0.9997366070747375 }, { "start": 12.78, "end": 13.26, "word": " bewältigen", "probability": 0.9998510479927063 }, { "start": 13.26, "end": 13.52, "word": " kann.", "probability": 0.9998555183410645 } ], "temperature": 0.0 }, { "id": 3, "seek": 0, "start": 13.86, "end": 15.98, "text": " Wie entstehen solche Datenmengen?", "tokens": [ 51023, 9233, 35955, 2932, 29813, 31126, 76, 1501, 268, 30, 51110 ], "avg_logprob": -0.0620685430864493, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00020198698621243238, "words": [ { "start": 13.86, "end": 14.5, "word": " Wie", "probability": 0.9872592091560364 }, { "start": 14.5, "end": 14.96, "word": " entstehen", "probability": 0.9997865259647369 }, { "start": 14.96, "end": 15.24, "word": " solche", "probability": 0.9976823329925537 }, { "start": 15.24, "end": 15.98, "word": " Datenmengen?", "probability": 0.9997863918542862 } ], "temperature": 0.0 }, { "id": 4, "seek": 0, "start": 16.36, "end": 24.02, "text": " Ganz einfach. 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begründen.", "probability": 0.9995790123939514 } ], "temperature": 0.0 } ], "params": { "language": "de", "task": "transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": null, "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "repetition": 3, "elapsed_seconds": 179.01268508518115, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware- beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Kräufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. Man kann sich daran die Finger verbrennen oder eine Zivilisation darauf begründen.", "segments": [ { "id": 1, "seek": 0, "start": 1.14, "end": 8.82, "text": " Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware-", "tokens": [ 50365, 5618, 879, 32783, 5429, 11888, 312, 32338, 7129, 31126, 76, 1501, 268, 11, 978, 370, 17253, 3290, 11, 2658, 587, 2804, 2194, 2710, 260, 16985, 12, 4513, 11817, 3039, 12, 50763 ], "avg_logprob": -0.0620685430864493, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00020198698621243238, "words": [ { "start": 1.14, "end": 1.64, "word": " Der", "probability": 0.9884618520736694 }, { "start": 1.64, "end": 2.1, "word": " Begriff", "probability": 0.9974938333034515 }, { "start": 2.1, "end": 2.58, "word": " Big", "probability": 0.9010727405548096 }, { "start": 2.58, "end": 2.88, "word": " Data", "probability": 0.9963210821151733 }, { "start": 2.88, "end": 4.2, "word": " bezeichnet", "probability": 0.9987662037213644 }, { "start": 4.2, "end": 4.88, "word": " Datenmengen,", "probability": 0.9997581094503403 }, { "start": 4.92, "end": 5.38, "word": " die", "probability": 0.9991471767425537 }, { "start": 5.38, "end": 5.56, "word": " so", "probability": 0.9996447563171387 }, { "start": 5.56, "end": 5.86, "word": " groß", "probability": 0.9921636581420898 }, { "start": 5.86, "end": 6.16, "word": " sind,", "probability": 0.9999066591262817 }, { "start": 6.28, "end": 6.6, "word": " dass", "probability": 0.9982548356056213 }, { "start": 6.6, "end": 6.74, "word": " man", "probability": 0.9997039437294006 }, { "start": 6.74, "end": 6.86, "word": " sie", "probability": 0.9991369843482971 }, { "start": 6.86, "end": 7.02, "word": " mit", "probability": 0.9995371103286743 }, { "start": 7.02, "end": 7.56, "word": " normaler", "probability": 0.9997589588165283 }, { "start": 7.56, "end": 7.8, "word": " Soft", "probability": 0.8935483694076538 }, { "start": 7.8, "end": 7.98, "word": "-", "probability": 0.9729250073432922 }, { "start": 7.98, "end": 8.2, "word": " oder", "probability": 0.9974942207336426 }, { "start": 8.2, "end": 8.68, "word": " Hardware", "probability": 0.9988400936126709 }, { "start": 8.68, "end": 8.82, "word": "-", "probability": 0.12491188198328018 } ], "temperature": 0.0 }, { "id": 2, "seek": 0, "start": 8.82, "end": 13.52, "text": " beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann.", "tokens": [ 50763, 312, 28213, 5846, 13109, 720, 34990, 2174, 10193, 25285, 268, 1163, 31126, 331, 24024, 1063, 1979, 5417, 17897, 15510, 3213, 4028, 13, 50991 ], "avg_logprob": -0.0620685430864493, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00020198698621243238, "words": [ { "start": 8.82, "end": 9.68, "word": " beziehungsweise", "probability": 0.8407757058739662 }, { "start": 9.68, "end": 10.3, "word": " herkömmlichen", "probability": 0.999100923538208 }, { "start": 10.3, "end": 10.84, "word": " Methoden", "probability": 0.9991872608661652 }, { "start": 10.84, "end": 10.94, "word": " der", "probability": 0.9995253086090088 }, { "start": 10.94, "end": 11.96, "word": " Datenverarbeitung", "probability": 0.9997965097427368 }, { "start": 11.96, "end": 12.48, "word": " nicht", "probability": 0.9916003942489624 }, { "start": 12.48, "end": 12.78, "word": " mehr", "probability": 0.9997366070747375 }, { "start": 12.78, "end": 13.26, "word": " bewältigen", "probability": 0.9998510479927063 }, { "start": 13.26, "end": 13.52, "word": " kann.", "probability": 0.9998555183410645 } ], "temperature": 0.0 }, { "id": 3, "seek": 0, "start": 13.86, "end": 15.98, "text": " Wie entstehen solche Datenmengen?", "tokens": [ 51023, 9233, 35955, 2932, 29813, 31126, 76, 1501, 268, 30, 51110 ], "avg_logprob": -0.0620685430864493, "compression_ratio": 1.4416666666666667, "no_speech_prob": 0.00020198698621243238, "words": [ { "start": 13.86, "end": 14.5, "word": " Wie", "probability": 0.9872592091560364 }, { "start": 14.5, "end": 14.96, "word": " entstehen", "probability": 0.9997865259647369 }, { "start": 14.96, "end": 15.24, "word": " solche", "probability": 0.9976823329925537 }, { "start": 15.24, "end": 15.98, "word": " Datenmengen?", "probability": 0.9997863918542862 } ], "temperature": 0.0 }, { "id": 4, "seek": 0, "start": 16.36, "end": 24.02, "text": " Ganz einfach. 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In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation. Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. Er fällt. Dabei gewinnt der Pendelkörper an Geschwindigkeit, so sehr, dass er in seiner ursprünglichen Ruhelage nicht etwa stehen bleibt, sondern einfach durchschwingt. Auf der anderen Seite bremst die Gravitation ihn wieder ab. Das Pendel kommt kurz zum Stehen. Und zwar auf derselben Höhe, auf der es gestartet wurde, und ändert die Richtung. Und so geht es immer hin und her. Ein Teil der Gravitationskraft wirkt seitlich als sogenannte Rückstellkraft, die das Pendel wieder in Richtung Ruhelage zieht. Der andere Teil hält den Faden des Pendels gespannt. So bewegt sich das Pendel auf einer kreisbogenförmigen Bahn. Eine komplette Schwingung des Pendels nennt man auch Periode. Die Anzahl der Schwingungen in einer bestimmten Zeit heißt Frequenz. Wie aber kommt das Pendel zum Stehen? Während das Pendel schwingt, wandeln sich kinetische Energie, also Bewegungsenergie und potenzielle Energie, auch Lageenergie genannt, ständig ineinander um. Die Summe aus beiden bleibt immer genau gleich, das Pendel dürfte also eigentlich nie mehr aufhören zu schwingen. Tatsächlich aber wird ein Teil der Energie in Wärme umgewandelt, durch die Reibung des Pendels an der Luft und am Aufhängepunkt. Diese Energie fehlt dann für die Schwingung. Dem Pendel geht praktisch der Schwung verloren. Und irgendwann kommt es zum Stehen.", "segments": [ { "id": 1, "seek": 0, "start": 0.59, "end": 6.85, "text": " Ein Pendel besteht in seiner einfachsten Form aus einem Faden und einem daran befestigten Pendelkörper.", "tokens": [ 50365, 6391, 38048, 338, 43680, 294, 23114, 7281, 6266, 10126, 3437, 6827, 479, 14771, 674, 6827, 24520, 21312, 377, 41498, 38048, 338, 74, 25533, 13, 50684 ], "avg_logprob": -0.07110163885535616, "compression_ratio": 1.6027397260273972, "no_speech_prob": 9.03010368347168e-05, "words": [ { "start": 0.59, "end": 1.07, "word": " Ein", "probability": 0.98681640625 }, { "start": 1.07, "end": 1.69, "word": " Pendel", "probability": 0.997314453125 }, { "start": 1.69, "end": 1.93, "word": " besteht", "probability": 0.9990234375 }, { "start": 1.93, "end": 2.23, "word": " in", "probability": 0.994140625 }, { "start": 2.23, "end": 2.41, "word": " seiner", "probability": 0.9990234375 }, { "start": 2.41, "end": 3.15, "word": " einfachsten", "probability": 0.99853515625 }, { "start": 3.15, "end": 3.43, "word": " Form", "probability": 0.9990234375 }, { "start": 3.43, "end": 3.69, "word": " aus", "probability": 0.99853515625 }, { "start": 3.69, "end": 3.93, "word": " einem", "probability": 0.99951171875 }, { "start": 3.93, "end": 4.39, "word": " Faden", "probability": 0.999755859375 }, { "start": 4.39, "end": 4.59, "word": " und", "probability": 0.98876953125 }, { "start": 4.59, "end": 4.85, "word": " einem", "probability": 0.99853515625 }, { "start": 4.85, "end": 5.19, "word": " daran", "probability": 0.9921875 }, { "start": 5.19, "end": 6.01, "word": " befestigten", "probability": 0.9991861979166666 }, { "start": 6.01, "end": 6.85, "word": " Pendelkörper.", "probability": 0.9996337890625 } ], "temperature": 0.0 }, { "id": 2, "seek": 0, "start": 6.99, "end": 13.11, "text": " In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation.", "tokens": [ 50684, 682, 497, 3232, 338, 609, 276, 43921, 38048, 338, 74, 25533, 674, 479, 14771, 12117, 720, 21777, 11, 5168, 25693, 18110, 8799, 2957, 1163, 8985, 85, 4614, 13, 51006 ], "avg_logprob": -0.07110163885535616, "compression_ratio": 1.6027397260273972, "no_speech_prob": 9.03010368347168e-05, "words": [ { "start": 6.99, "end": 7.37, "word": " In", "probability": 0.998046875 }, { "start": 7.37, "end": 8.07, "word": " Ruhelage", "probability": 0.9981689453125 }, { "start": 8.07, "end": 8.43, "word": " hängen", "probability": 0.99951171875 }, { "start": 8.43, "end": 9.19, "word": " Pendelkörper", "probability": 0.9998779296875 }, { "start": 9.19, "end": 9.45, "word": " und", "probability": 0.99951171875 }, { "start": 9.45, "end": 9.87, "word": " Faden", "probability": 0.99951171875 }, { "start": 9.87, "end": 10.15, "word": " gerade", "probability": 0.998046875 }, { "start": 10.15, "end": 10.79, "word": " herunter,", "probability": 0.9970703125 }, { "start": 10.89, "end": 11.31, "word": " nach", "probability": 0.99951171875 }, { "start": 11.31, "end": 11.63, "word": " unten", "probability": 0.99951171875 }, { "start": 11.63, "end": 12.09, "word": " gezogen", "probability": 0.99951171875 }, { "start": 12.09, "end": 12.31, "word": " von", "probability": 0.9990234375 }, { "start": 12.31, "end": 12.47, "word": " der", "probability": 1.0 }, { "start": 12.47, "end": 13.11, "word": " Gravitation.", "probability": 0.9993489583333334 } ], "temperature": 0.0 }, { "id": 3, "seek": 0, "start": 13.86, "end": 22.49, "text": " Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. 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Wie aber kommt das Pendel zum Stehen? Während das Pendel schwingt, wandeln sich kinetische Energie, also Bewegungsenergie und potenzielle Energie, auch Lageenergie genannt, ständig ineinander um. Die Summe aus beiden bleibt immer genau gleich, das Pendel dürfte also eigentlich nie mehr aufhören zu schwingen. Tatsächlich aber wird ein Teil der Energie in Wärme umgewandelt, durch die Reibung des Pendels an der Luft und am Aufhängepunkt. Diese Energie fehlt dann für die Schwingung. Dem Pendel geht praktisch der Schwung verloren. 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Wie aber kommt das Pendel zum Stehen? Während das Pendel schwingt, wandeln sich kinetische Energie, also Bewegungsenergie und potenzielle Energie, auch Lageenergie genannt, ständig ineinander um. Die Summe aus beiden bleibt immer genau gleich, das Pendel dürfte also eigentlich nie mehr aufhören zu schwingen. Tatsächlich aber wird ein Teil der Energie in Wärme umgewandelt, durch die Reibung des Pendels an der Luft und am Aufhängepunkt. Diese Energie fehlt dann für die Schwingung. Dem Pendel geht praktisch der Schwung verloren. 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Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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"audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "clip": "big-data", "repetition": 2, "runtime_seconds": 5.568429544997343, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware- beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? 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"start": 122.06, "end": 130.42, "text": " Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden.", "tokens": [ 51081, 27577, 6310, 5429, 11888, 36905, 11, 1105, 14280, 38192, 18021, 36272, 11368, 425, 25657, 674, 11793, 18844, 11, 43932, 46820, 5084, 674, 14255, 36776, 2501, 2804, 2164, 16236, 339, 716, 4380, 13, 51490 ], "avg_logprob": -0.08915844187140465, "compression_ratio": 1.625, "no_speech_prob": 0.00490570068359375, "words": [ { "start": 122.06, "end": 122.7, "word": " Unternehmen", "probability": 0.99951171875 }, { "start": 122.7, "end": 123.08, "word": " können", "probability": 1.0 }, { "start": 123.08, "end": 123.34, "word": " Big", "probability": 0.99609375 }, { "start": 123.34, "end": 123.56, "word": " Data", "probability": 0.9892578125 }, { "start": 123.56, "end": 123.84, "word": " nutzen,", "probability": 0.9990234375 }, { "start": 124.16, "end": 124.6, "word": " um", "probability": 1.0 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0.9998046875 } ], "temperature": 0.0 }, { "id": 22, "seek": 10406, "start": 130.64, "end": 132.88, "text": " Und das sind nur einige Beispiele.", "tokens": [ 51519, 2719, 1482, 3290, 4343, 28338, 879, 7631, 15949, 13, 51611 ], "avg_logprob": -0.08915844187140465, "compression_ratio": 1.625, "no_speech_prob": 0.00490570068359375, "words": [ { "start": 130.64, "end": 131.18, "word": " Und", "probability": 0.99853515625 }, { "start": 131.18, "end": 131.4, "word": " das", "probability": 1.0 }, { "start": 131.4, "end": 131.88, "word": " sind", "probability": 1.0 }, { "start": 131.88, "end": 132.04, "word": " nur", "probability": 1.0 }, { "start": 132.04, "end": 132.3, "word": " einige", "probability": 0.99951171875 }, { "start": 132.3, "end": 132.88, "word": " Beispiele.", "probability": 0.9998372395833334 } ], "temperature": 0.0 }, { "id": 23, "seek": 10406, "start": 133.36, "end": 135.86, "text": " Aber Big Data wirft auch Fragen auf.", "tokens": [ 51643, 5992, 5429, 11888, 1987, 844, 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Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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0.9998046875 } ], "temperature": 0.0 }, { "id": 22, "seek": 10406, "start": 130.64, "end": 132.88, "text": " Und das sind nur einige Beispiele.", "tokens": [ 51519, 2719, 1482, 3290, 4343, 28338, 879, 7631, 15949, 13, 51611 ], "avg_logprob": -0.08915844187140465, "compression_ratio": 1.625, "no_speech_prob": 0.00490570068359375, "words": [ { "start": 130.64, "end": 131.18, "word": " Und", "probability": 0.99853515625 }, { "start": 131.18, "end": 131.4, "word": " das", "probability": 1.0 }, { "start": 131.4, "end": 131.88, "word": " sind", "probability": 1.0 }, { "start": 131.88, "end": 132.04, "word": " nur", "probability": 1.0 }, { "start": 132.04, "end": 132.3, "word": " einige", "probability": 0.99951171875 }, { "start": 132.3, "end": 132.88, "word": " Beispiele.", "probability": 0.9998372395833334 } ], "temperature": 0.0 }, { "id": 23, "seek": 10406, "start": 133.36, "end": 135.86, "text": " Aber Big Data wirft auch Fragen auf.", "tokens": [ 51643, 5992, 5429, 11888, 1987, 844, 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"audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "clip": "pendel", "repetition": 1, "runtime_seconds": 3.657605626998702, "text": "Ein Pendel besteht in seiner einfachsten Form aus einem Faden und einem daran befestigten Pendelkörper. In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation. Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. Er fällt. Dabei gewinnt der Pendelkörper an Geschwindigkeit, so sehr, dass er in seiner ursprünglichen Ruhelage nicht etwa stehen bleibt, sondern einfach durchschwingt. Auf der anderen Seite bremst die Gravitation ihn wieder ab. Das Pendel kommt kurz zum Stehen. Und zwar auf derselben Höhe, auf der es gestartet wurde, und ändert die Richtung. Und so geht es immer hin und her. Ein Teil der Gravitationskraft wirkt seitlich als sogenannte Rückstellkraft, die das Pendel wieder in Richtung Ruhelage zieht. Der andere Teil hält den Faden des Pendels gespannt. So bewegt sich das Pendel auf einer kreisbogenförmigen Bahn. Eine komplette Schwingung des Pendels nennt man auch Periode. Die Anzahl der Schwingungen in einer bestimmten Zeit heißt Frequenz. Wie aber kommt das Pendel zum Stehen? Während das Pendel schwingt, wandeln sich kinetische Energie, also Bewegungsenergie und potenzielle Energie, auch Lageenergie genannt, ständig ineinander um. Die Summe aus beiden bleibt immer genau gleich, das Pendel dürfte also eigentlich nie mehr aufhören zu schwingen. Tatsächlich aber wird ein Teil der Energie in Wärme umgewandelt, durch die Reibung des Pendels an der Luft und am Aufhängepunkt. Diese Energie fehlt dann für die Schwingung. Dem Pendel geht praktisch der Schwung verloren. Und irgendwann kommt es zum Stehen.", "segments": [ { "id": 1, "seek": 0, "start": 0.59, "end": 6.85, "text": " Ein Pendel besteht in seiner einfachsten Form aus einem Faden und einem daran befestigten Pendelkörper.", "tokens": [ 50365, 6391, 38048, 338, 43680, 294, 23114, 7281, 6266, 10126, 3437, 6827, 479, 14771, 674, 6827, 24520, 21312, 377, 41498, 38048, 338, 74, 25533, 13, 50684 ], "avg_logprob": -0.0643544802159974, "compression_ratio": 1.6027397260273972, "no_speech_prob": 0.0004477500915527344, "words": [ { "start": 0.59, "end": 1.07, "word": " Ein", "probability": 0.98681640625 }, { "start": 1.07, "end": 1.69, "word": " Pendel", "probability": 0.997314453125 }, { "start": 1.69, "end": 1.93, "word": " besteht", "probability": 0.9990234375 }, { "start": 1.93, "end": 2.23, "word": " in", "probability": 0.994140625 }, { "start": 2.23, "end": 2.41, "word": " seiner", "probability": 0.9990234375 }, { "start": 2.41, "end": 3.15, "word": " einfachsten", "probability": 0.99853515625 }, { "start": 3.15, "end": 3.43, "word": " Form", "probability": 0.9990234375 }, { "start": 3.43, "end": 3.69, "word": " aus", "probability": 0.99853515625 }, { "start": 3.69, "end": 3.93, "word": " einem", "probability": 0.99951171875 }, { "start": 3.93, "end": 4.39, "word": " Faden", "probability": 0.999755859375 }, { "start": 4.39, "end": 4.59, "word": " und", "probability": 0.98876953125 }, { "start": 4.59, "end": 4.85, "word": " einem", "probability": 0.99853515625 }, { "start": 4.85, "end": 5.19, "word": " daran", "probability": 0.9921875 }, { "start": 5.19, "end": 6.01, "word": " befestigten", "probability": 0.9991861979166666 }, { "start": 6.01, "end": 6.85, "word": " Pendelkörper.", "probability": 0.9996337890625 } ], "temperature": 0.0 }, { "id": 2, "seek": 0, "start": 6.99, "end": 13.11, "text": " In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation.", "tokens": [ 50684, 682, 497, 3232, 338, 609, 276, 43921, 38048, 338, 74, 25533, 674, 479, 14771, 12117, 720, 21777, 11, 5168, 25693, 18110, 8799, 2957, 1163, 8985, 85, 4614, 13, 51006 ], "avg_logprob": -0.0643544802159974, "compression_ratio": 1.6027397260273972, "no_speech_prob": 0.0004477500915527344, "words": [ { "start": 6.99, "end": 7.37, "word": " In", "probability": 0.998046875 }, { "start": 7.37, "end": 8.07, "word": " Ruhelage", "probability": 0.9981689453125 }, { "start": 8.07, "end": 8.43, "word": " hängen", "probability": 0.99951171875 }, { "start": 8.43, "end": 9.19, "word": " Pendelkörper", "probability": 0.9998779296875 }, { "start": 9.19, "end": 9.45, "word": " und", "probability": 0.99951171875 }, { "start": 9.45, "end": 9.87, "word": " Faden", "probability": 0.99951171875 }, { "start": 9.87, "end": 10.15, "word": " gerade", "probability": 0.998046875 }, { "start": 10.15, "end": 10.79, "word": " herunter,", "probability": 0.9970703125 }, { "start": 10.89, "end": 11.31, "word": " nach", "probability": 0.99951171875 }, { "start": 11.31, "end": 11.63, "word": " unten", "probability": 0.99951171875 }, { "start": 11.63, "end": 12.09, "word": " gezogen", "probability": 0.99951171875 }, { "start": 12.09, "end": 12.31, "word": " von", "probability": 0.9990234375 }, { "start": 12.31, "end": 12.47, "word": " der", "probability": 1.0 }, { "start": 12.47, "end": 13.11, "word": " Gravitation.", "probability": 0.9993489583333334 } ], "temperature": 0.0 }, { "id": 3, "seek": 0, "start": 13.86, "end": 22.49, "text": " Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. 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In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation. Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. Er fällt. Dabei gewinnt der Pendelkörper an Geschwindigkeit, so sehr, dass er in seiner ursprünglichen Ruhelage nicht etwa stehen bleibt, sondern einfach durchschwingt. Auf der anderen Seite bremst die Gravitation ihn wieder ab. Das Pendel kommt kurz zum Stehen. Und zwar auf derselben Höhe, auf der es gestartet wurde, und ändert die Richtung. Und so geht es immer hin und her. Ein Teil der Gravitationskraft wirkt seitlich als sogenannte Rückstellkraft, die das Pendel wieder in Richtung Ruhelage zieht. Der andere Teil hält den Faden des Pendels gespannt. So bewegt sich das Pendel auf einer kreisbogenförmigen Bahn. Eine komplette Schwingung des Pendels nennt man auch Periode. Die Anzahl der Schwingungen in einer bestimmten Zeit heißt Frequenz. Wie aber kommt das Pendel zum Stehen? Während das Pendel schwingt, wandeln sich kinetische Energie, also Bewegungsenergie und potenzielle Energie, auch Lageenergie genannt, ständig ineinander um. Die Summe aus beiden bleibt immer genau gleich, das Pendel dürfte also eigentlich nie mehr aufhören zu schwingen. Tatsächlich aber wird ein Teil der Energie in Wärme umgewandelt, durch die Reibung des Pendels an der Luft und am Aufhängepunkt. Diese Energie fehlt dann für die Schwingung. Dem Pendel geht praktisch der Schwung verloren. 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In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation. Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. Er fällt. Dabei gewinnt der Pendelkörper an Geschwindigkeit, so sehr, dass er in seiner ursprünglichen Ruhelage nicht etwa stehen bleibt, sondern einfach durchschwingt. Auf der anderen Seite bremst die Gravitation ihn wieder ab. Das Pendel kommt kurz zum Stehen. Und zwar auf derselben Höhe, auf der es gestartet wurde, und ändert die Richtung. Und so geht es immer hin und her. Ein Teil der Gravitationskraft wirkt seitlich als sogenannte Rückstellkraft, die das Pendel wieder in Richtung Ruhelage zieht. Der andere Teil hält den Faden des Pendels gespannt. So bewegt sich das Pendel auf einer kreisbogenförmigen Bahn. Eine komplette Schwingung des Pendels nennt man auch Periode. Die Anzahl der Schwingungen in einer bestimmten Zeit heißt Frequenz. Wie aber kommt das Pendel zum Stehen? Während das Pendel schwingt, wandeln sich kinetische Energie, also Bewegungsenergie und potenzielle Energie, auch Lageenergie genannt, ständig ineinander um. Die Summe aus beiden bleibt immer genau gleich, das Pendel dürfte also eigentlich nie mehr aufhören zu schwingen. Tatsächlich aber wird ein Teil der Energie in Wärme umgewandelt, durch die Reibung des Pendels an der Luft und am Aufhängepunkt. Diese Energie fehlt dann für die Schwingung. Dem Pendel geht praktisch der Schwung verloren. 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Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Onlineshopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die häufig persönlichen Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Onlineshopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die häufig persönlichen Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? 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Aber Big Data wirft auch Fragen auf.", "tokens": [ 51521, 2719, 1482, 3290, 4343, 28338, 879, 7631, 15949, 13, 5992, 5429, 11888, 1987, 844, 2168, 25588, 2501, 13, 51767 ], "avg_logprob": -0.08412388732124652, "compression_ratio": 1.625, "no_speech_prob": 0.0150604248046875, "words": [ { "start": 130.64, "end": 131.18, "word": " Und", "probability": 0.99658203125 }, { "start": 131.18, "end": 131.4, "word": " das", "probability": 1.0 }, { "start": 131.4, "end": 131.88, "word": " sind", "probability": 0.99951171875 }, { "start": 131.88, "end": 132.04, "word": " nur", "probability": 1.0 }, { "start": 132.04, "end": 132.3, "word": " einige", "probability": 0.99951171875 }, { "start": 132.3, "end": 132.88, "word": " Beispiele.", "probability": 0.9998372395833334 }, { "start": 133.04, "end": 133.68, "word": " Aber", "probability": 0.99951171875 }, { "start": 133.68, "end": 133.92, "word": " Big", "probability": 0.99951171875 }, { "start": 133.92, "end": 134.2, "word": " Data", "probability": 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Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick, jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, Jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Onlineshopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die häufig persönlichen Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? 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In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation. Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. Er fällt. Dabei gewinnt der Pendelkörper an Geschwindigkeit, so sehr, dass er in seiner ursprünglichen Ruhelage nicht etwa stehen bleibt, sondern einfach durchschwingt. Auf der anderen Seite bremst die Gravitation ihn wieder ab. Das Pendel kommt kurz zum Stehen. Und zwar auf derselben Höhe, auf der es gestartet wurde, und ändert die Richtung. Und so geht es immer hin und her. Ein Teil der Gravitationskraft wirkt seitlich als sogenannte Rückstellkraft, die das Pendel wieder in Richtung Ruhelage zieht. Der andere Teil hält den Faden des Pendels gespannt. So bewegt sich das Pendel auf einer kreisbogenförmigen Bahn. Eine komplette Schwingung des Pendels nennt man auch Periode. Die Anzahl der Schwingungen in einer bestimmten Zeit heißt Frequenz. Wie aber kommt das Pendel zum Stehen? Während das Pendel schwingt, wandeln sich kinetische Energie, also Bewegungsenergie und potenzielle Energie, auch Lageenergie genannt, ständig ineinander um. Die Summe aus beiden bleibt immer genau gleich. Das Pendel dürfte also eigentlich nie mehr aufhören zu schwingen. Tatsächlich aber wird ein Teil der Energie in Wärme umgewandelt, durch die Reibung des Pendels an der Luft und am Aufhängepunkt. Diese Energie fehlt dann für die Schwingung. 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In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation. Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. Er fällt. Dabei gewinnt der Pendelkörper an Geschwindigkeit, so sehr, dass er in seiner ursprünglichen Ruhelage nicht etwa stehen bleibt, sondern einfach durchschwingt. Auf der anderen Seite bremst die Gravitation ihn wieder ab. Das Pendel kommt kurz zum Stehen. Und zwar auf derselben Höhe, auf der es gestartet wurde, und ändert die Richtung. Und so geht es immer hin und her. Ein Teil der Gravitationskraft wirkt seitlich als sogenannte Rückstellkraft, die das Pendel wieder in Richtung Ruhelage zieht. Der andere Teil hält den Faden des Pendels gespannt. So bewegt sich das Pendel auf einer kreisbogenförmigen Bahn. Eine komplette Schwingung des Pendels nennt man auch Periode. Die Anzahl der Schwingungen in einer bestimmten Zeit heißt Frequenz. Wie aber kommt das Pendel zum Stehen? Während das Pendel schwingt, wandeln sich kinetische Energie, also Bewegungsenergie und potenzielle Energie, auch Lageenergie genannt, ständig ineinander um. Die Summe aus beiden bleibt immer genau gleich. Das Pendel dürfte also eigentlich nie mehr aufhören zu schwingen. Tatsächlich aber wird ein Teil der Energie in Wärme umgewandelt, durch die Reibung des Pendels an der Luft und am Aufhängepunkt. Diese Energie fehlt dann für die Schwingung. 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In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation. Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. Er fällt. Dabei gewinnt der Pendelkörper an Geschwindigkeit so sehr, dass er in seiner ursprünglichen Ruhelage nicht etwa stehen bleibt, sondern einfach durchschwingt. Auf der anderen Seite bremst die Gravitation ihn wieder ab. Das Pendel kommt kurz zum Stehen – und zwar auf derselben Höhe, auf der es gestartet wurde – und ändert die Richtung – und so geht es immer hin und her. Ein Teil der Gravitationskraft wirkt seitlich als sogenannte Rückstellkraft, die das Pendel wieder in Richtung Ruhelage zieht. Der andere Teil hält den Faden des Pendels gespannt. So bewegt sich das Pendel auf einer kreisbogenförmigen Bahn. Eine komplette Schwingung des Pendels nennt man auch Periode. Die Anzahl der Schwingungen in einer bestimmten Zeit heißt Frequenz. 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"audio_sha256": "ced3021531f9de9b47b367cbfa70a7f5f02f6d0004dfdffe0bfafc5567f339d5", "source_sha256": "dac0c2eebb2aacc9557bd83589217f8bd09c68d1592c3a3f8d4562645a6ce66b" }, { "clip": "pendel", "repetition": 2, "runtime_seconds": 1.6705243850010447, "text": "Ein Pendel besteht in seiner einfachsten Form aus einem Faden und einem daran befestigten Pendelkörper. 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Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick. Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, Jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? 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kontrollieren?", "tokens": [ 51548, 12810, 785, 41484, 1739, 11, 4797, 4343, 11472, 3969, 19691, 27577, 2804, 47107, 5695, 30, 51759 ], "avg_logprob": -0.11692040649234739, "compression_ratio": 1.5977859778597785, "no_speech_prob": 0.0, "words": [ { "start": 139.48, "end": 140.12, "word": " Ist", "probability": 0.99853515625 }, { "start": 140.12, "end": 140.28, "word": " es", "probability": 1.0 }, { "start": 140.28, "end": 140.9, "word": " gefährlich,", "probability": 1.0 }, { "start": 141.06, "end": 141.32, "word": " wenn", "probability": 1.0 }, { "start": 141.32, "end": 141.48, "word": " nur", "probability": 1.0 }, { "start": 141.48, "end": 141.92, "word": " wenige", "probability": 0.999755859375 }, { "start": 141.92, "end": 142.3, "word": " große", "probability": 0.99755859375 }, { "start": 142.3, "end": 142.82, "word": " Unternehmen", "probability": 1.0 }, { "start": 142.82, "end": 143.0, "word": " sie", "probability": 0.98388671875 }, { "start": 143.0, "end": 143.62, "word": " 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"word": " Man", "probability": 0.99951171875 }, { "start": 168.88, "end": 169.08, "word": " kann", "probability": 1.0 }, { "start": 169.08, "end": 169.26, "word": " sich", "probability": 1.0 }, { "start": 169.26, "end": 169.48, "word": " daran", "probability": 1.0 }, { "start": 169.48, "end": 169.64, "word": " die", "probability": 1.0 }, { "start": 169.64, "end": 169.88, "word": " Finger", "probability": 1.0 }, { "start": 169.88, "end": 170.4, "word": " verbrennen", "probability": 0.9996744791666666 }, { "start": 170.4, "end": 170.88, "word": " oder", "probability": 0.998046875 }, { "start": 170.88, "end": 171.18, "word": " eine", "probability": 0.99951171875 }, { "start": 171.18, "end": 171.9, "word": " Zivilisation", "probability": 0.9998372395833334 }, { "start": 171.9, "end": 172.22, "word": " darauf", "probability": 1.0 }, { "start": 172.22, "end": 172.7, "word": " begründen.", "probability": 0.999755859375 } ], "temperature": 0.0 } ], "params": { "language": "de", "task": "transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": "Big Data, Datenanalyse, Onlineshop, Kaufempfehlungen.", "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "clip": "big-data", "repetition": 2, "runtime_seconds": 2.247011168001336, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick. Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, Jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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kontrollieren?", "tokens": [ 51548, 12810, 785, 41484, 1739, 11, 4797, 4343, 11472, 3969, 19691, 27577, 2804, 47107, 5695, 30, 51759 ], "avg_logprob": -0.11692040649234739, "compression_ratio": 1.5977859778597785, "no_speech_prob": 0.0, "words": [ { "start": 139.48, "end": 140.12, "word": " Ist", "probability": 0.99853515625 }, { "start": 140.12, "end": 140.28, "word": " es", "probability": 1.0 }, { "start": 140.28, "end": 140.9, "word": " gefährlich,", "probability": 1.0 }, { "start": 141.06, "end": 141.32, "word": " wenn", "probability": 1.0 }, { "start": 141.32, "end": 141.48, "word": " nur", "probability": 1.0 }, { "start": 141.48, "end": 141.92, "word": " wenige", "probability": 0.999755859375 }, { "start": 141.92, "end": 142.3, "word": " große", "probability": 0.99755859375 }, { "start": 142.3, "end": 142.82, "word": " Unternehmen", "probability": 1.0 }, { "start": 142.82, "end": 143.0, "word": " sie", "probability": 0.98388671875 }, { "start": 143.0, "end": 143.62, "word": " 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"transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": "Big Data, Datenanalyse, Onlineshop, Kaufempfehlungen.", "hotwords": null, "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "clip": "big-data", "repetition": 3, "runtime_seconds": 2.290787456004182, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick. Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, Jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen, und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Online-Shopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? 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medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden.", "tokens": [ 51505, 682, 1163, 1205, 36367, 6282, 42938, 1063, 6310, 10482, 521, 7186, 29966, 11, 978, 30930, 36812, 1053, 2959, 48896, 38754, 3355, 23625, 929, 2164, 20734, 13, 51793 ], "avg_logprob": -0.07963709473129242, "compression_ratio": 1.5109034267912773, "no_speech_prob": 0.0, "words": [ { "start": 106.91, "end": 107.17, "word": " In", "probability": 1.0 }, { "start": 107.17, "end": 107.31, "word": " der", "probability": 1.0 }, { "start": 107.31, "end": 107.93, "word": " medizinischen", "probability": 1.0 }, { "start": 107.93, "end": 108.41, "word": " Forschung", "probability": 1.0 }, { "start": 108.41, "end": 108.71, "word": " können", "probability": 1.0 }, { "start": 108.71, "end": 109.35, "word": " Massendaten", "probability": 0.9990234375 }, { "start": 109.35, "end": 109.69, "word": " helfen,", "probability": 1.0 }, { "start": 109.93, "end": 110.29, "word": " 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"probability": 1.0 } ], "temperature": 0.0 }, { "id": 28, "seek": 11178, "start": 136.52, "end": 139.22, "text": " Wem gehören die, häufig persönlichen, Daten?", "tokens": [ 51426, 343, 443, 13218, 26377, 978, 11, 47543, 31228, 10193, 11, 31126, 30, 51539 ], "avg_logprob": -0.11692040649234739, "compression_ratio": 1.5977859778597785, "no_speech_prob": 0.0, "words": [ { "start": 136.52, "end": 137.18, "word": " Wem", "probability": 0.989013671875 }, { "start": 137.18, "end": 137.52, "word": " gehören", "probability": 1.0 }, { "start": 137.52, "end": 137.72, "word": " die,", "probability": 1.0 }, { "start": 137.74, "end": 138.12, "word": " häufig", "probability": 1.0 }, { "start": 138.12, "end": 138.86, "word": " persönlichen,", "probability": 0.996826171875 }, { "start": 138.98, "end": 139.22, "word": " Daten?", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 29, "seek": 11178, "start": 139.48, "end": 143.62, "text": " Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren?", "tokens": [ 51548, 12810, 785, 41484, 1739, 11, 4797, 4343, 11472, 3969, 19691, 27577, 2804, 47107, 5695, 30, 51759 ], "avg_logprob": -0.11692040649234739, "compression_ratio": 1.5977859778597785, "no_speech_prob": 0.0, "words": [ { "start": 139.48, "end": 140.12, "word": " Ist", "probability": 0.99853515625 }, { "start": 140.12, "end": 140.28, "word": " es", "probability": 1.0 }, { "start": 140.28, "end": 140.9, "word": " gefährlich,", "probability": 1.0 }, { "start": 141.06, "end": 141.32, "word": " wenn", "probability": 1.0 }, { "start": 141.32, "end": 141.48, "word": " nur", "probability": 1.0 }, { "start": 141.48, "end": 141.92, "word": " wenige", "probability": 0.999755859375 }, { "start": 141.92, "end": 142.3, "word": " große", "probability": 0.99755859375 }, { "start": 142.3, "end": 142.82, "word": " Unternehmen", "probability": 1.0 }, { "start": 142.82, "end": 143.0, "word": " sie", "probability": 0.98388671875 }, { "start": 143.0, "end": 143.62, "word": " kontrollieren?", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 30, "seek": 13964, "start": 144.4, "end": 146.58, "text": " Wollen wir, und das ist ein reales Beispiel,", "tokens": [ 50405, 343, 26669, 1987, 11, 674, 1482, 1418, 1343, 957, 279, 13772, 11, 50515 ], "avg_logprob": -0.11737351231868305, "compression_ratio": 1.5066666666666666, "no_speech_prob": 0.0, "words": [ { "start": 144.4, "end": 144.7, "word": " Wollen", "probability": 0.994384765625 }, { "start": 144.7, "end": 144.98, "word": " wir,", "probability": 1.0 }, { "start": 145.14, "end": 145.36, "word": " und", "probability": 0.99951171875 }, { "start": 145.36, "end": 145.52, "word": " das", "probability": 1.0 }, { "start": 145.52, "end": 145.66, "word": " ist", "probability": 1.0 }, { "start": 145.66, "end": 145.8, "word": " ein", "probability": 1.0 }, { "start": 145.8, "end": 146.22, "word": " reales", "probability": 0.99951171875 }, { "start": 146.22, "end": 146.58, "word": " Beispiel,", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 31, "seek": 13964, "start": 147.32, "end": 152.22, "text": " wirklich von einem Online-Shop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist?", "tokens": [ 50553, 9696, 2957, 6827, 16930, 12, 7774, 404, 2501, 5859, 271, 730, 44590, 331, 20731, 694, 49472, 11, 2658, 21717, 17932, 3176, 1418, 30, 50795 ], "avg_logprob": -0.11737351231868305, "compression_ratio": 1.5066666666666666, "no_speech_prob": 0.0, "words": [ { "start": 147.32, "end": 147.64, "word": " wirklich", "probability": 0.9990234375 }, { "start": 147.64, "end": 147.9, "word": " von", "probability": 1.0 }, { "start": 147.9, "end": 148.18, "word": " einem", "probability": 1.0 }, { "start": 148.18, "end": 148.5, "word": " Online", "probability": 0.5986328125 }, { "start": 148.5, "end": 148.84, "word": "-Shop", "probability": 0.99853515625 }, { "start": 148.84, "end": 149.08, "word": " auf", "probability": 1.0 }, { "start": 149.08, "end": 149.52, "word": " Basis", "probability": 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], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "ced3021531f9de9b47b367cbfa70a7f5f02f6d0004dfdffe0bfafc5567f339d5", "source_sha256": "dac0c2eebb2aacc9557bd83589217f8bd09c68d1592c3a3f8d4562645a6ce66b" }, { "clip": "pendel", "repetition": 2, "runtime_seconds": 1.5243343230031314, "text": "Ein Pendel besteht in seiner einfachsten Form aus einem Faden und einem daran befestigten Pendelkörper. In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation. Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. Er fällt. Dabei gewinnt der Pendelkörper an Geschwindigkeit so sehr, dass er in seiner ursprünglichen Ruhelage nicht etwa stehen bleibt, sondern einfach durchschwingt. Auf der anderen Seite bremst die Gravitation ihn wieder ab. Das Pendel kommt kurz zum Stehen – und zwar auf derselben Höhe, auf der es gestartet wurde – und ändert die Richtung – und so geht es immer hin und her. Ein Teil der Gravitationskraft wirkt seitlich als sogenannte Rückstellkraft, die das Pendel wieder in Richtung Ruhelage zieht. Der andere Teil hält den Faden des Pendels gespannt. So bewegt sich das Pendel auf einer kreisbogenförmigen Bahn. Eine komplette Schwingung des Pendels nennt man auch Periode. Die Anzahl der Schwingungen in einer bestimmten Zeit heißt Frequenz. Wie aber kommt das Pendel zum Stehen? Während das Pendel schwingt, wandeln sich kinetische Energie, also Bewegungsenergie, und potenzielle Energie, auch Lageenergie genannt, ständig ineinander um. Die Summe aus beiden bleibt immer genau gleich. Das Pendel dürfte also eigentlich nie mehr aufhören zu schwingen. Tatsächlich aber wird ein Teil der Energie in Wärme umgewandelt, durch die Reibung des Pendels an der Luft und am Aufhängenpunkt. Diese Energie fehlt dann für die Schwingung. 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In Ruhelage hängen Pendelkörper und Faden gerade herunter, nach unten gezogen von der Gravitation. Wird das Pendel durch eine seitliche Auslenkung des Fadens angestoßen, zieht die Gravitation auch jetzt den Pendelkörper nach unten. Er fällt. Dabei gewinnt der Pendelkörper an Geschwindigkeit so sehr, dass er in seiner ursprünglichen Ruhelage nicht etwa stehen bleibt, sondern einfach durchschwingt. Auf der anderen Seite bremst die Gravitation ihn wieder ab. Das Pendel kommt kurz zum Stehen – und zwar auf derselben Höhe, auf der es gestartet wurde – und ändert die Richtung – und so geht es immer hin und her. Ein Teil der Gravitationskraft wirkt seitlich als sogenannte Rückstellkraft, die das Pendel wieder in Richtung Ruhelage zieht. Der andere Teil hält den Faden des Pendels gespannt. So bewegt sich das Pendel auf einer kreisbogenförmigen Bahn. Eine komplette Schwingung des Pendels nennt man auch Periode. Die Anzahl der Schwingungen in einer bestimmten Zeit heißt Frequenz. 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Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick. Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, Jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Onlineshopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. Man kann sich daran die Finger verbrennen oder eine Zivilisation darauf begründen.", "segments": [ { "id": 1, "seek": 0, "start": 1.14, "end": 8.8, "text": " Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware", "tokens": [ 50365, 5618, 879, 32783, 5429, 11888, 312, 32338, 7129, 31126, 76, 1501, 268, 11, 978, 370, 17253, 3290, 11, 2658, 587, 2804, 2194, 2710, 260, 16985, 12, 4513, 11817, 3039, 50749 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 1.14, "end": 1.7, "word": " Der", "probability": 0.98681640625 }, { "start": 1.7, "end": 2.14, "word": " Begriff", "probability": 0.99951171875 }, { "start": 2.14, "end": 2.66, "word": " Big", "probability": 0.87255859375 }, { "start": 2.66, "end": 3.08, "word": " Data", "probability": 0.98583984375 }, { "start": 3.08, "end": 4.24, "word": " bezeichnet", "probability": 0.9990234375 }, { "start": 4.24, "end": 4.96, "word": " Datenmengen,", "probability": 0.99951171875 }, { "start": 5.06, "end": 5.44, "word": " die", "probability": 0.9990234375 }, { "start": 5.44, "end": 5.68, "word": " so", "probability": 0.99951171875 }, { "start": 5.68, "end": 6.0, "word": " groß", "probability": 0.9990234375 }, { "start": 6.0, "end": 6.3, "word": " sind,", "probability": 1.0 }, { "start": 6.54, "end": 6.66, "word": " dass", "probability": 0.9970703125 }, { "start": 6.66, "end": 6.8, "word": " man", "probability": 1.0 }, { "start": 6.8, "end": 6.94, "word": " sie", "probability": 0.99951171875 }, { "start": 6.94, "end": 7.08, "word": " mit", "probability": 1.0 }, { "start": 7.08, "end": 7.66, "word": " normaler", "probability": 0.999755859375 }, { "start": 7.66, "end": 7.94, "word": " Soft", "probability": 0.91162109375 }, { "start": 7.94, "end": 8.12, "word": "-", "probability": 0.94970703125 }, { "start": 8.12, "end": 8.28, "word": " oder", "probability": 0.9951171875 }, { "start": 8.28, "end": 8.8, "word": " Hardware", "probability": 0.99951171875 } ], "temperature": 0.0 }, { "id": 2, "seek": 0, "start": 8.8, "end": 13.64, "text": " beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann.", "tokens": [ 50749, 312, 28213, 5846, 13109, 720, 34990, 2174, 10193, 25285, 268, 1163, 31126, 331, 24024, 1063, 1979, 5417, 17897, 15510, 3213, 4028, 13, 50992 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 8.8, "end": 9.76, "word": " beziehungsweise", "probability": 0.81304931640625 }, { "start": 9.76, "end": 10.42, "word": " herkömmlichen", "probability": 0.9998779296875 }, { "start": 10.42, "end": 10.86, "word": " Methoden", "probability": 0.99951171875 }, { "start": 10.86, "end": 11.0, "word": " der", "probability": 0.99951171875 }, { "start": 11.0, "end": 12.02, "word": " Datenverarbeitung", "probability": 1.0 }, { "start": 12.02, "end": 12.62, "word": " nicht", "probability": 0.99853515625 }, { "start": 12.62, "end": 12.84, "word": " mehr", "probability": 1.0 }, { "start": 12.84, "end": 13.38, "word": " bewältigen", "probability": 0.9998372395833334 }, { "start": 13.38, "end": 13.64, "word": " kann.", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 3, "seek": 0, "start": 14.02, "end": 16.02, "text": " Wie entstehen solche Datenmengen?", "tokens": [ 51033, 9233, 35955, 2932, 29813, 31126, 76, 1501, 268, 30, 51110 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 14.02, "end": 14.6, "word": " Wie", "probability": 0.9892578125 }, { "start": 14.6, "end": 15.02, "word": " entstehen", "probability": 1.0 }, { "start": 15.02, "end": 15.32, "word": " solche", "probability": 1.0 }, { "start": 15.32, "end": 16.02, "word": " Datenmengen?", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 4, "seek": 0, "start": 16.69, "end": 19.98, "text": " Ganz einfach. Wir alle speisen sie mit jedem Klick.", "tokens": [ 51151, 32496, 7281, 13, 4347, 5430, 768, 11106, 2804, 2194, 36538, 16053, 618, 13, 51307 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 16.69, "end": 17.08, "word": " Ganz", "probability": 0.99755859375 }, { "start": 17.08, "end": 17.48, "word": " einfach.", "probability": 1.0 }, { "start": 17.84, "end": 18.28, "word": " Wir", "probability": 0.99951171875 }, { "start": 18.28, "end": 18.48, "word": " alle", "probability": 0.998046875 }, { "start": 18.48, "end": 18.94, "word": " speisen", "probability": 0.99951171875 }, { "start": 18.94, "end": 19.1, "word": " sie", "probability": 0.8916015625 }, { "start": 19.1, "end": 19.32, "word": " mit", "probability": 0.99951171875 }, { "start": 19.32, "end": 19.62, "word": " jedem", "probability": 1.0 }, { "start": 19.62, "end": 19.98, "word": " Klick.", "probability": 0.999267578125 } ], "temperature": 0.0 }, { "id": 5, "seek": 0, "start": 20.18, "end": 29.14, "text": " Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch,", "tokens": [ 51307, 27076, 443, 16930, 12, 36, 475, 9507, 11, 19610, 462, 278, 4488, 1028, 9219, 328, 763, 1321, 3628, 11, 19610, 3773, 282, 2682, 25392, 5886, 313, 11, 36538, 14889, 14338, 267, 11, 36538, 45750, 28349, 1004, 12, 33, 279, 625, 11, 51766 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 20.18, "end": 20.74, "word": " Jedem", "probability": 0.985107421875 }, { "start": 20.74, "end": 21.04, "word": " Online", "probability": 0.9970703125 }, { "start": 21.04, "end": 21.6, "word": "-Einkauf,", "probability": 0.997802734375 }, { "start": 21.76, "end": 22.26, "word": " jeder", "probability": 0.84521484375 }, { "start": 22.26, "end": 22.72, "word": " Eingabe", "probability": 0.9996744791666666 }, { "start": 22.72, "end": 22.88, "word": " ins", "probability": 0.99951171875 }, { "start": 22.88, "end": 24.02, "word": " Navigationsgerät,", "probability": 0.9994140625 }, { "start": 24.54, "end": 24.72, "word": " jeder", "probability": 0.9912109375 }, { "start": 24.72, "end": 25.84, "word": " Finanztransaktion,", "probability": 0.9984537760416666 }, { "start": 26.2, "end": 26.62, "word": " jedem", "probability": 0.99755859375 }, { "start": 26.62, "end": 27.34, "word": " Telefonat,", "probability": 0.9998372395833334 }, { "start": 27.82, "end": 28.02, "word": " jedem", "probability": 0.998046875 }, { "start": 28.02, "end": 28.74, "word": " Fitnessstudio", "probability": 0.99609375 }, { "start": 28.74, "end": 29.14, "word": "-Besuch,", "probability": 0.992431640625 } ], "temperature": 0.0 }, { "id": 6, "seek": 2800, "start": 29.14, "end": 36.88, "text": " Jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant.", "tokens": [ 50365, 27076, 443, 21387, 29685, 566, 31541, 268, 38889, 26833, 13, 2846, 27018, 11, 978, 31126, 607, 432, 261, 31625, 13, 2719, 19054, 367, 10633, 13, 50754 ], "avg_logprob": -0.13364955658713976, "compression_ratio": 1.4354066985645932, "no_speech_prob": 0.0, "words": [ { "start": 29.14, "end": 30.24, "word": " Jedem", "probability": 0.72412109375 }, { "start": 30.24, "end": 30.48, "word": " neuen", "probability": 1.0 }, { "start": 30.48, "end": 30.88, "word": " Freund", "probability": 0.99951171875 }, { "start": 30.88, "end": 31.18, "word": " im", "probability": 0.99951171875 }, { "start": 31.18, "end": 31.8, "word": " sozialen", "probability": 0.998291015625 }, { "start": 31.8, "end": 32.32, "word": " Netzwerk.", "probability": 1.0 }, { "start": 32.5, "end": 33.18, "word": " Das", "probability": 0.9990234375 }, { "start": 33.18, "end": 33.64, "word": " bedeutet,", "probability": 1.0 }, { "start": 34.04, "end": 34.5, "word": " die", "probability": 0.99951171875 }, { "start": 34.5, "end": 35.2, "word": " Datenberge", "probability": 0.9993489583333334 }, { "start": 35.2, "end": 35.58, "word": " wachsen.", "probability": 1.0 }, { "start": 35.78, "end": 36.22, "word": " Und", "probability": 0.99951171875 }, { "start": 36.22, "end": 36.44, "word": " zwar", "probability": 1.0 }, { "start": 36.44, "end": 36.88, "word": " rasant.", "probability": 0.999755859375 } ], "temperature": 0.0 }, { "id": 7, "seek": 2800, "start": 38.77, "end": 44.75, "text": " Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat,", "tokens": [ 50794, 3229, 11699, 68, 40723, 364, 31126, 11, 978, 978, 27773, 8480, 2957, 30859, 9533, 18193, 1063, 364, 7393, 5919, 11674, 17822, 13511, 19182, 2385, 11, 51078 ], "avg_logprob": -0.13364955658713976, "compression_ratio": 1.4354066985645932, "no_speech_prob": 0.0, "words": [ { "start": 38.77, "end": 39.33, "word": " Die", "probability": 0.9990234375 }, { "start": 39.33, "end": 39.79, "word": " gleiche", "probability": 0.9990234375 }, { "start": 39.79, "end": 39.91, "word": " Menge", "probability": 0.99951171875 }, { "start": 39.91, "end": 40.11, "word": " an", "probability": 1.0 }, { "start": 40.11, "end": 40.45, "word": " Daten,", "probability": 1.0 }, { "start": 40.67, "end": 41.03, "word": " die", "probability": 1.0 }, { "start": 41.03, "end": 41.17, "word": " die", "probability": 1.0 }, { "start": 41.17, "end": 41.55, "word": " Menschheit", "probability": 0.999755859375 }, { "start": 41.55, "end": 41.71, "word": " von", "probability": 0.99951171875 }, { "start": 41.71, "end": 41.93, "word": " ihrem", "probability": 0.9990234375 }, { "start": 41.93, "end": 42.39, "word": " Ursprung", "probability": 0.9996744791666666 }, { "start": 42.39, "end": 42.61, "word": " an", "probability": 0.99951171875 }, { "start": 42.61, "end": 42.91, "word": " bis", "probability": 0.99951171875 }, { "start": 42.91, "end": 43.13, "word": " zum", "probability": 1.0 }, { "start": 43.13, "end": 43.29, "word": " Jahr", "probability": 0.9990234375 }, { "start": 43.29, "end": 43.93, "word": " 2002", "probability": 0.99951171875 }, { "start": 43.93, "end": 44.47, "word": " geschaffen", "probability": 0.999755859375 }, { "start": 44.47, "end": 44.75, "word": " hat,", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 8, "seek": 2800, "start": 45.37, "end": 52.29, "text": " entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten.", "tokens": [ 51110, 35955, 357, 566, 11674, 8227, 23703, 294, 1163, 9394, 11, 294, 1163, 2804, 4891, 591, 2518, 1653, 504, 35061, 13, 36705, 13209, 2957, 1266, 27593, 13, 51456 ], "avg_logprob": -0.13364955658713976, "compression_ratio": 1.4354066985645932, "no_speech_prob": 0.0, "words": [ { "start": 45.37, "end": 45.77, "word": " entsteht", "probability": 0.999755859375 }, { "start": 45.77, "end": 45.91, "word": " im", "probability": 0.99951171875 }, { "start": 45.91, "end": 46.05, "word": " Jahr", "probability": 1.0 }, { "start": 46.05, "end": 46.93, "word": " 2014", "probability": 0.99951171875 }, { "start": 46.93, "end": 47.63, "word": " bereits", "probability": 0.99951171875 }, { "start": 47.63, "end": 47.83, "word": " in", "probability": 1.0 }, { "start": 47.83, "end": 48.01, "word": " der", "probability": 1.0 }, { "start": 48.01, "end": 48.39, "word": " Zeit,", "probability": 0.99951171875 }, { "start": 48.57, "end": 48.89, "word": " in", 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Aber Big Data wirft auch Fragen auf.", "tokens": [ 51515, 2719, 1482, 3290, 4343, 28338, 879, 7631, 15949, 13, 5992, 5429, 11888, 1987, 844, 2168, 25588, 2501, 13, 51761 ], "avg_logprob": -0.07721819116600923, "compression_ratio": 1.625, "no_speech_prob": 0.0, "words": [ { "start": 130.76, "end": 131.26, "word": " Und", "probability": 0.99951171875 }, { "start": 131.26, "end": 131.54, "word": " das", "probability": 1.0 }, { "start": 131.54, "end": 131.96, "word": " sind", "probability": 1.0 }, { "start": 131.96, "end": 132.1, "word": " nur", "probability": 1.0 }, { "start": 132.1, "end": 132.44, "word": " einige", "probability": 1.0 }, { "start": 132.44, "end": 132.96, "word": " Beispiele.", "probability": 0.9996744791666666 }, { "start": 133.22, "end": 133.78, "word": " Aber", "probability": 0.99951171875 }, { "start": 133.78, "end": 134.0, "word": " Big", "probability": 1.0 }, { "start": 134.0, "end": 134.4, "word": " Data", "probability": 1.0 }, { "start": 134.4, "end": 135.06, 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"temperature": 0.0 } ], "params": { "language": "de", "task": "transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": null, "hotwords": "Big Data, Datenanalyse, Onlineshop, Kaufempfehlungen.", "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "clip": "big-data", "repetition": 2, "runtime_seconds": 2.1486123759968905, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick. Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, Jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Onlineshopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. 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Wir alle speisen sie mit jedem Klick.", "tokens": [ 51151, 32496, 7281, 13, 4347, 5430, 768, 11106, 2804, 2194, 36538, 16053, 618, 13, 51307 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 16.69, "end": 17.08, "word": " Ganz", "probability": 0.99755859375 }, { "start": 17.08, "end": 17.48, "word": " einfach.", "probability": 1.0 }, { "start": 17.84, "end": 18.28, "word": " Wir", "probability": 0.99951171875 }, { "start": 18.28, "end": 18.48, "word": " alle", "probability": 0.998046875 }, { "start": 18.48, "end": 18.94, "word": " speisen", "probability": 0.99951171875 }, { "start": 18.94, "end": 19.1, "word": " sie", "probability": 0.8916015625 }, { "start": 19.1, "end": 19.32, "word": " mit", "probability": 0.99951171875 }, { "start": 19.32, "end": 19.62, "word": " jedem", "probability": 1.0 }, { "start": 19.62, "end": 19.98, "word": " Klick.", "probability": 0.999267578125 } ], "temperature": 0.0 }, { "id": 5, "seek": 0, "start": 20.18, "end": 29.14, "text": " Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch,", "tokens": [ 51307, 27076, 443, 16930, 12, 36, 475, 9507, 11, 19610, 462, 278, 4488, 1028, 9219, 328, 763, 1321, 3628, 11, 19610, 3773, 282, 2682, 25392, 5886, 313, 11, 36538, 14889, 14338, 267, 11, 36538, 45750, 28349, 1004, 12, 33, 279, 625, 11, 51766 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 20.18, "end": 20.74, "word": " Jedem", "probability": 0.985107421875 }, { "start": 20.74, "end": 21.04, "word": " Online", "probability": 0.9970703125 }, { "start": 21.04, "end": 21.6, "word": "-Einkauf,", "probability": 0.997802734375 }, { "start": 21.76, "end": 22.26, "word": " jeder", "probability": 0.84521484375 }, { "start": 22.26, "end": 22.72, "word": " Eingabe", "probability": 0.9996744791666666 }, { "start": 22.72, "end": 22.88, "word": " ins", "probability": 0.99951171875 }, { "start": 22.88, "end": 24.02, "word": " Navigationsgerät,", "probability": 0.9994140625 }, { "start": 24.54, "end": 24.72, "word": " jeder", "probability": 0.9912109375 }, { "start": 24.72, "end": 25.84, "word": " Finanztransaktion,", "probability": 0.9984537760416666 }, { "start": 26.2, "end": 26.62, "word": " jedem", "probability": 0.99755859375 }, { "start": 26.62, "end": 27.34, "word": " Telefonat,", "probability": 0.9998372395833334 }, { "start": 27.82, "end": 28.02, "word": " jedem", "probability": 0.998046875 }, { "start": 28.02, "end": 28.74, "word": " Fitnessstudio", "probability": 0.99609375 }, { "start": 28.74, "end": 29.14, "word": "-Besuch,", "probability": 0.992431640625 } ], "temperature": 0.0 }, { "id": 6, "seek": 2800, "start": 29.14, "end": 36.88, "text": " Jedem neuen Freund im sozialen Netzwerk. 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Aber Big Data wirft auch Fragen auf.", "tokens": [ 51515, 2719, 1482, 3290, 4343, 28338, 879, 7631, 15949, 13, 5992, 5429, 11888, 1987, 844, 2168, 25588, 2501, 13, 51761 ], "avg_logprob": -0.07721819116600923, "compression_ratio": 1.625, "no_speech_prob": 0.0, "words": [ { "start": 130.76, "end": 131.26, "word": " Und", "probability": 0.99951171875 }, { "start": 131.26, "end": 131.54, "word": " das", "probability": 1.0 }, { "start": 131.54, "end": 131.96, "word": " sind", "probability": 1.0 }, { "start": 131.96, "end": 132.1, "word": " nur", "probability": 1.0 }, { "start": 132.1, "end": 132.44, "word": " einige", "probability": 1.0 }, { "start": 132.44, "end": 132.96, "word": " Beispiele.", "probability": 0.9996744791666666 }, { "start": 133.22, "end": 133.78, "word": " Aber", "probability": 0.99951171875 }, { "start": 133.78, "end": 134.0, "word": " Big", "probability": 1.0 }, { "start": 134.0, "end": 134.4, "word": " Data", "probability": 1.0 }, { "start": 134.4, "end": 135.06, 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"temperature": 0.0 } ], "params": { "language": "de", "task": "transcribe", "beam_size": 5, "best_of": 5, "vad_filter": true, "word_timestamps": true, "condition_on_previous_text": false, "initial_prompt": null, "hotwords": "Big Data, Datenanalyse, Onlineshop, Kaufempfehlungen.", "temperature": [ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 ], "compression_ratio_threshold": 2.4, "log_prob_threshold": -1.0, "no_speech_threshold": 0.6 }, "audio_sha256": "5ad312990e5ec0100b376b0c909971346a5813b43cbdf7e7855ff2bef3cd8d62", "source_sha256": "636ee0afe3d3c79d96d790db801780c8a64337b600e1fdc7b312e32744c18feb" }, { "clip": "big-data", "repetition": 3, "runtime_seconds": 2.1499202429986326, "text": "Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann. Wie entstehen solche Datenmengen? Ganz einfach. Wir alle speisen sie mit jedem Klick. Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch, Jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant. Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat, entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. Innerhalb von 10 Minuten. Mit dem Begriff Big Data sind aber häufig nicht nur die Daten an sich, sondern auch deren Analyse und Nutzung gemeint. Das bedeutet, dass man die Datenmenge nutzt, indem man darin automatisiert Muster und Zusammenhänge sucht. Ein Beispiel dafür kennt fast jeder vom Onlineshopping. Empfehlungen à la Kunden, die das kauften, kauften auch, basieren auf der Echtzeitauswertung von Millionen von Kaufdaten anderer Kunden. Mithilfe dieser Daten wird errechnet, welche Produkte den Käufer mit hoher Wahrscheinlichkeit noch interessieren könnten. Ein ganz anderes Beispiel für die Analyse von Big Data kommt aus den USA. Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt. Es sind unendlich viele Bereiche denkbar, in denen Big Data zum Einsatz kommen kann. In der medizinischen Forschung können Massendaten helfen, die besten Therapien für Krankheiten wie Krebs zu finden. Maschinen können ihre eigenen Betriebsdaten nutzen, um daraus zu lernen und effizienter zu werden. Unternehmen können Big Data nutzen, um ihre Kunden besser kennenzulernen und Produkte, Dienstleistungen und Werbung auf sie zuzuschneiden. Und das sind nur einige Beispiele. Aber Big Data wirft auch Fragen auf. Wem gehören die, häufig persönlichen, Daten? Ist es gefährlich, wenn nur wenige große Unternehmen sie kontrollieren? Wollen wir, und das ist ein reales Beispiel, wirklich von einem Onlineshop auf Basis des Kaufverhaltens erfahren, dass jemand schwanger ist? Und kommt irgendwann der Zeitpunkt, an dem auch menschliches Verhalten voraussagbar wird? Also, Big Data bietet enorme Chancen, aber auch Risiken. Es ist ein wenig wie bei der Entdeckung des Feuers durch die Steinzeitmenschen. Man kann sich daran die Finger verbrennen oder eine Zivilisation darauf begründen.", "segments": [ { "id": 1, "seek": 0, "start": 1.14, "end": 8.8, "text": " Der Begriff Big Data bezeichnet Datenmengen, die so groß sind, dass man sie mit normaler Soft- oder Hardware", "tokens": [ 50365, 5618, 879, 32783, 5429, 11888, 312, 32338, 7129, 31126, 76, 1501, 268, 11, 978, 370, 17253, 3290, 11, 2658, 587, 2804, 2194, 2710, 260, 16985, 12, 4513, 11817, 3039, 50749 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 1.14, "end": 1.7, "word": " Der", "probability": 0.98681640625 }, { "start": 1.7, "end": 2.14, "word": " Begriff", "probability": 0.99951171875 }, { "start": 2.14, "end": 2.66, "word": " Big", "probability": 0.87255859375 }, { "start": 2.66, "end": 3.08, "word": " Data", "probability": 0.98583984375 }, { "start": 3.08, "end": 4.24, "word": " bezeichnet", "probability": 0.9990234375 }, { "start": 4.24, "end": 4.96, "word": " Datenmengen,", "probability": 0.99951171875 }, { "start": 5.06, "end": 5.44, "word": " die", "probability": 0.9990234375 }, { "start": 5.44, "end": 5.68, "word": " so", "probability": 0.99951171875 }, { "start": 5.68, "end": 6.0, "word": " groß", "probability": 0.9990234375 }, { "start": 6.0, "end": 6.3, "word": " sind,", "probability": 1.0 }, { "start": 6.54, "end": 6.66, "word": " dass", "probability": 0.9970703125 }, { "start": 6.66, "end": 6.8, "word": " man", "probability": 1.0 }, { "start": 6.8, "end": 6.94, "word": " sie", "probability": 0.99951171875 }, { "start": 6.94, "end": 7.08, "word": " mit", "probability": 1.0 }, { "start": 7.08, "end": 7.66, "word": " normaler", "probability": 0.999755859375 }, { "start": 7.66, "end": 7.94, "word": " Soft", "probability": 0.91162109375 }, { "start": 7.94, "end": 8.12, "word": "-", "probability": 0.94970703125 }, { "start": 8.12, "end": 8.28, "word": " oder", "probability": 0.9951171875 }, { "start": 8.28, "end": 8.8, "word": " Hardware", "probability": 0.99951171875 } ], "temperature": 0.0 }, { "id": 2, "seek": 0, "start": 8.8, "end": 13.64, "text": " beziehungsweise herkömmlichen Methoden der Datenverarbeitung nicht mehr bewältigen kann.", "tokens": [ 50749, 312, 28213, 5846, 13109, 720, 34990, 2174, 10193, 25285, 268, 1163, 31126, 331, 24024, 1063, 1979, 5417, 17897, 15510, 3213, 4028, 13, 50992 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 8.8, "end": 9.76, "word": " beziehungsweise", "probability": 0.81304931640625 }, { "start": 9.76, "end": 10.42, "word": " herkömmlichen", "probability": 0.9998779296875 }, { "start": 10.42, "end": 10.86, "word": " Methoden", "probability": 0.99951171875 }, { "start": 10.86, "end": 11.0, "word": " der", "probability": 0.99951171875 }, { "start": 11.0, "end": 12.02, "word": " Datenverarbeitung", "probability": 1.0 }, { "start": 12.02, "end": 12.62, "word": " nicht", "probability": 0.99853515625 }, { "start": 12.62, "end": 12.84, "word": " mehr", "probability": 1.0 }, { "start": 12.84, "end": 13.38, "word": " bewältigen", "probability": 0.9998372395833334 }, { "start": 13.38, "end": 13.64, "word": " kann.", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 3, "seek": 0, "start": 14.02, "end": 16.02, "text": " Wie entstehen solche Datenmengen?", "tokens": [ 51033, 9233, 35955, 2932, 29813, 31126, 76, 1501, 268, 30, 51110 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 14.02, "end": 14.6, "word": " Wie", "probability": 0.9892578125 }, { "start": 14.6, "end": 15.02, "word": " entstehen", "probability": 1.0 }, { "start": 15.02, "end": 15.32, "word": " solche", "probability": 1.0 }, { "start": 15.32, "end": 16.02, "word": " Datenmengen?", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 4, "seek": 0, "start": 16.69, "end": 19.98, "text": " Ganz einfach. Wir alle speisen sie mit jedem Klick.", "tokens": [ 51151, 32496, 7281, 13, 4347, 5430, 768, 11106, 2804, 2194, 36538, 16053, 618, 13, 51307 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 16.69, "end": 17.08, "word": " Ganz", "probability": 0.99755859375 }, { "start": 17.08, "end": 17.48, "word": " einfach.", "probability": 1.0 }, { "start": 17.84, "end": 18.28, "word": " Wir", "probability": 0.99951171875 }, { "start": 18.28, "end": 18.48, "word": " alle", "probability": 0.998046875 }, { "start": 18.48, "end": 18.94, "word": " speisen", "probability": 0.99951171875 }, { "start": 18.94, "end": 19.1, "word": " sie", "probability": 0.8916015625 }, { "start": 19.1, "end": 19.32, "word": " mit", "probability": 0.99951171875 }, { "start": 19.32, "end": 19.62, "word": " jedem", "probability": 1.0 }, { "start": 19.62, "end": 19.98, "word": " Klick.", "probability": 0.999267578125 } ], "temperature": 0.0 }, { "id": 5, "seek": 0, "start": 20.18, "end": 29.14, "text": " Jedem Online-Einkauf, jeder Eingabe ins Navigationsgerät, jeder Finanztransaktion, jedem Telefonat, jedem Fitnessstudio-Besuch,", "tokens": [ 51307, 27076, 443, 16930, 12, 36, 475, 9507, 11, 19610, 462, 278, 4488, 1028, 9219, 328, 763, 1321, 3628, 11, 19610, 3773, 282, 2682, 25392, 5886, 313, 11, 36538, 14889, 14338, 267, 11, 36538, 45750, 28349, 1004, 12, 33, 279, 625, 11, 51766 ], "avg_logprob": -0.09672618692829496, "compression_ratio": 1.509090909090909, "no_speech_prob": 0.0, "words": [ { "start": 20.18, "end": 20.74, "word": " Jedem", "probability": 0.985107421875 }, { "start": 20.74, "end": 21.04, "word": " Online", "probability": 0.9970703125 }, { "start": 21.04, "end": 21.6, "word": "-Einkauf,", "probability": 0.997802734375 }, { "start": 21.76, "end": 22.26, "word": " jeder", "probability": 0.84521484375 }, { "start": 22.26, "end": 22.72, "word": " Eingabe", "probability": 0.9996744791666666 }, { "start": 22.72, "end": 22.88, "word": " ins", "probability": 0.99951171875 }, { "start": 22.88, "end": 24.02, "word": " Navigationsgerät,", "probability": 0.9994140625 }, { "start": 24.54, "end": 24.72, "word": " jeder", "probability": 0.9912109375 }, { "start": 24.72, "end": 25.84, "word": " Finanztransaktion,", "probability": 0.9984537760416666 }, { "start": 26.2, "end": 26.62, "word": " jedem", "probability": 0.99755859375 }, { "start": 26.62, "end": 27.34, "word": " Telefonat,", "probability": 0.9998372395833334 }, { "start": 27.82, "end": 28.02, "word": " jedem", "probability": 0.998046875 }, { "start": 28.02, "end": 28.74, "word": " Fitnessstudio", "probability": 0.99609375 }, { "start": 28.74, "end": 29.14, "word": "-Besuch,", "probability": 0.992431640625 } ], "temperature": 0.0 }, { "id": 6, "seek": 2800, "start": 29.14, "end": 36.88, "text": " Jedem neuen Freund im sozialen Netzwerk. Das bedeutet, die Datenberge wachsen. Und zwar rasant.", "tokens": [ 50365, 27076, 443, 21387, 29685, 566, 31541, 268, 38889, 26833, 13, 2846, 27018, 11, 978, 31126, 607, 432, 261, 31625, 13, 2719, 19054, 367, 10633, 13, 50754 ], "avg_logprob": -0.13364955658713976, "compression_ratio": 1.4354066985645932, "no_speech_prob": 0.0, "words": [ { "start": 29.14, "end": 30.24, "word": " Jedem", "probability": 0.72412109375 }, { "start": 30.24, "end": 30.48, "word": " neuen", "probability": 1.0 }, { "start": 30.48, "end": 30.88, "word": " Freund", "probability": 0.99951171875 }, { "start": 30.88, "end": 31.18, "word": " im", "probability": 0.99951171875 }, { "start": 31.18, "end": 31.8, "word": " sozialen", "probability": 0.998291015625 }, { "start": 31.8, "end": 32.32, "word": " Netzwerk.", "probability": 1.0 }, { "start": 32.5, "end": 33.18, "word": " Das", "probability": 0.9990234375 }, { "start": 33.18, "end": 33.64, "word": " bedeutet,", "probability": 1.0 }, { "start": 34.04, "end": 34.5, "word": " die", "probability": 0.99951171875 }, { "start": 34.5, "end": 35.2, "word": " Datenberge", "probability": 0.9993489583333334 }, { "start": 35.2, "end": 35.58, "word": " wachsen.", "probability": 1.0 }, { "start": 35.78, "end": 36.22, "word": " Und", "probability": 0.99951171875 }, { "start": 36.22, "end": 36.44, "word": " zwar", "probability": 1.0 }, { "start": 36.44, "end": 36.88, "word": " rasant.", "probability": 0.999755859375 } ], "temperature": 0.0 }, { "id": 7, "seek": 2800, "start": 38.77, "end": 44.75, "text": " Die gleiche Menge an Daten, die die Menschheit von ihrem Ursprung an bis zum Jahr 2002 geschaffen hat,", "tokens": [ 50794, 3229, 11699, 68, 40723, 364, 31126, 11, 978, 978, 27773, 8480, 2957, 30859, 9533, 18193, 1063, 364, 7393, 5919, 11674, 17822, 13511, 19182, 2385, 11, 51078 ], "avg_logprob": -0.13364955658713976, "compression_ratio": 1.4354066985645932, "no_speech_prob": 0.0, "words": [ { "start": 38.77, "end": 39.33, "word": " Die", "probability": 0.9990234375 }, { "start": 39.33, "end": 39.79, "word": " gleiche", "probability": 0.9990234375 }, { "start": 39.79, "end": 39.91, "word": " Menge", "probability": 0.99951171875 }, { "start": 39.91, "end": 40.11, "word": " an", "probability": 1.0 }, { "start": 40.11, "end": 40.45, "word": " Daten,", "probability": 1.0 }, { "start": 40.67, "end": 41.03, "word": " die", "probability": 1.0 }, { "start": 41.03, "end": 41.17, "word": " die", "probability": 1.0 }, { "start": 41.17, "end": 41.55, "word": " Menschheit", "probability": 0.999755859375 }, { "start": 41.55, "end": 41.71, "word": " von", "probability": 0.99951171875 }, { "start": 41.71, "end": 41.93, "word": " ihrem", "probability": 0.9990234375 }, { "start": 41.93, "end": 42.39, "word": " Ursprung", "probability": 0.9996744791666666 }, { "start": 42.39, "end": 42.61, "word": " an", "probability": 0.99951171875 }, { "start": 42.61, "end": 42.91, "word": " bis", "probability": 0.99951171875 }, { "start": 42.91, "end": 43.13, "word": " zum", "probability": 1.0 }, { "start": 43.13, "end": 43.29, "word": " Jahr", "probability": 0.9990234375 }, { "start": 43.29, "end": 43.93, "word": " 2002", "probability": 0.99951171875 }, { "start": 43.93, "end": 44.47, "word": " geschaffen", "probability": 0.999755859375 }, { "start": 44.47, "end": 44.75, "word": " hat,", "probability": 1.0 } ], "temperature": 0.0 }, { "id": 8, "seek": 2800, "start": 45.37, "end": 52.29, "text": " entsteht im Jahr 2014 bereits in der Zeit, in der sie einen Kaffee trinken. 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94.91, "end": 101.45, "text": " Dort wurde über die automatisierte Auswertung von Tweets die Verbreitung einer Grippewelle vorhergesagt und eingedämmt.", "tokens": [ 50998, 32308, 11191, 4502, 978, 28034, 271, 23123, 9039, 26521, 1063, 2957, 47763, 1385, 978, 4281, 2672, 35563, 6850, 46082, 19833, 826, 2447, 29195, 2880, 7665, 674, 17002, 292, 737, 2174, 83, 13, 51324 ], "avg_logprob": -0.0806074769140404, "compression_ratio": 1.4568345323741008, "no_speech_prob": 0.0, "words": [ { "start": 94.91, "end": 95.19, "word": " Dort", "probability": 0.99951171875 }, { "start": 95.19, "end": 95.47, "word": " wurde", "probability": 0.99951171875 }, { "start": 95.47, "end": 95.65, "word": " über", "probability": 0.99951171875 }, { "start": 95.65, "end": 95.79, "word": " die", "probability": 0.99951171875 }, { "start": 95.79, "end": 96.59, "word": " automatisierte", "probability": 0.9998372395833334 }, { "start": 96.59, "end": 97.17, "word": " Auswertung", "probability": 0.9998372395833334 }, { 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Aber Big Data wirft auch Fragen auf.", "tokens": [ 51515, 2719, 1482, 3290, 4343, 28338, 879, 7631, 15949, 13, 5992, 5429, 11888, 1987, 844, 2168, 25588, 2501, 13, 51761 ], "avg_logprob": -0.07721819116600923, "compression_ratio": 1.625, "no_speech_prob": 0.0, "words": [ { "start": 130.76, "end": 131.26, "word": " Und", "probability": 0.99951171875 }, { "start": 131.26, "end": 131.54, "word": " das", "probability": 1.0 }, { "start": 131.54, "end": 131.96, "word": " sind", "probability": 1.0 }, { "start": 131.96, "end": 132.1, "word": " nur", "probability": 1.0 }, { "start": 132.1, "end": 132.44, "word": " einige", "probability": 1.0 }, { "start": 132.44, "end": 132.96, "word": " Beispiele.", "probability": 0.9996744791666666 }, { "start": 133.22, "end": 133.78, "word": " Aber", "probability": 0.99951171875 }, { "start": 133.78, "end": 134.0, "word": " Big", "probability": 1.0 }, { "start": 134.0, "end": 134.4, "word": " Data", "probability": 1.0 }, { "start": 134.4, "end": 135.06, 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