Hulu and Hang: Understanding Viewer Sessions with Big Data
Everyday, Hulu ingests 100 terabytes of user level app interaction data. This session data is the closest touchpoint we have to subscribers' experience in our product short of joining them on their couch in their living rooms. Making meaning out of session data is a non-trivial effort across da...
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Körperschaft: | |
| Format: | Elektronisch Video |
| Sprache: | Englisch |
| Veröffentlicht: |
[Erscheinungsort nicht ermittelbar]
Data Science Salon
2019
|
| Ausgabe: | 1st edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/00000ZXK1SDIF0BE/?ar |
| Zusammenfassung: | Everyday, Hulu ingests 100 terabytes of user level app interaction data. This session data is the closest touchpoint we have to subscribers' experience in our product short of joining them on their couch in their living rooms. Making meaning out of session data is a non-trivial effort across data instrumentation, engineering, analytics, and data science teams. In this talk, you will get an inside look at how we are tackling this monumental project at Hulu: from product design, to generating insights, to building predictive models - all to create the most personalized and engaging streaming experience for our subscribers. |
| Beschreibung: | Online resource; Title from title screen (viewed September 10, 2019) |
| Umfang: | 1 Online-Ressource (1 video file, aSeitenSeitenroximately 29 min.) |
| Format: | Mode of access: World Wide Web. |
Internformat
MARC
| LEADER | 00000cgm a22000002c 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-054866413 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228121138.0 | ||
| 006 | m o | | | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 200807s2019 xx ||| |o o ||eng c | ||
| 035 | |a (DE-627-1)054866413 | ||
| 035 | |a (DE-599)KEP054866413 | ||
| 035 | |a (ORHE)00000ZXK1SDIF0BE | ||
| 035 | |a (DE-627-1)054866413 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | |a E VIDEO | |
| 100 | 1 | |a Beley, Catherine |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Hulu and Hang |b Understanding Viewer Sessions with Big Data |c Beley, Catherine |
| 250 | |a 1st edition. | ||
| 264 | 1 | |a [Erscheinungsort nicht ermittelbar] |b Data Science Salon |c 2019 | |
| 264 | 2 | |a Boston, MA |b Safari. | |
| 300 | |a 1 Online-Ressource (1 video file, aSeitenSeitenroximately 29 min.) | ||
| 336 | |a zweidimensionales bewegtes Bild |b tdi |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Online resource; Title from title screen (viewed September 10, 2019) | ||
| 520 | |a Everyday, Hulu ingests 100 terabytes of user level app interaction data. This session data is the closest touchpoint we have to subscribers' experience in our product short of joining them on their couch in their living rooms. Making meaning out of session data is a non-trivial effort across data instrumentation, engineering, analytics, and data science teams. In this talk, you will get an inside look at how we are tackling this monumental project at Hulu: from product design, to generating insights, to building predictive models - all to create the most personalized and engaging streaming experience for our subscribers. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 630 | 2 | 0 | |a Hulu |
| 650 | 0 | |a Consumer satisfaction | |
| 650 | 0 | |a Streaming technology (Telecommunications) | |
| 650 | 0 | |a Big data | |
| 650 | 4 | |a Consommateurs ; Satisfaction | |
| 650 | 4 | |a En continu (Télécommunications) | |
| 650 | 4 | |a Données volumineuses | |
| 650 | 4 | |a Electronic videos | |
| 700 | 1 | |a Huff, Herbie |e VerfasserIn |4 aut | |
| 710 | 2 | |a Safari, an O'Reilly Media Company. |e MitwirkendeR |4 ctb | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/00000ZXK1SDIF0BE/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 935 | |c vide | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-054866413 |
|---|---|
| _version_ | 1829007852408143873 |
| adam_text | |
| any_adam_object | |
| author | Beley, Catherine Huff, Herbie |
| author_corporate | Safari, an O'Reilly Media Company |
| author_corporate_role | ctb |
| author_facet | Beley, Catherine Huff, Herbie Safari, an O'Reilly Media Company |
| author_role | aut aut |
| author_sort | Beley, Catherine |
| author_variant | c b cb h h hh |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)054866413 (DE-599)KEP054866413 (ORHE)00000ZXK1SDIF0BE |
| dewey-raw | E VIDEO |
| dewey-search | E VIDEO |
| edition | 1st edition. |
| format | Electronic Video |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02362cgm a22004932c 4500</leader><controlfield tag="001">ZDB-30-ORH-054866413</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228121138.0</controlfield><controlfield tag="006">m o | | </controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">200807s2019 xx ||| |o o ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)054866413</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP054866413</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)00000ZXK1SDIF0BE</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)054866413</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">E VIDEO</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Beley, Catherine</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Hulu and Hang</subfield><subfield code="b">Understanding Viewer Sessions with Big Data</subfield><subfield code="c">Beley, Catherine</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st edition.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Erscheinungsort nicht ermittelbar]</subfield><subfield code="b">Data Science Salon</subfield><subfield code="c">2019</subfield></datafield><datafield tag="264" ind1=" " ind2="2"><subfield code="a">Boston, MA</subfield><subfield code="b">Safari.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (1 video file, aSeitenSeitenroximately 29 min.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">zweidimensionales bewegtes Bild</subfield><subfield code="b">tdi</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Online resource; Title from title screen (viewed September 10, 2019)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Everyday, Hulu ingests 100 terabytes of user level app interaction data. This session data is the closest touchpoint we have to subscribers' experience in our product short of joining them on their couch in their living rooms. Making meaning out of session data is a non-trivial effort across data instrumentation, engineering, analytics, and data science teams. In this talk, you will get an inside look at how we are tackling this monumental project at Hulu: from product design, to generating insights, to building predictive models - all to create the most personalized and engaging streaming experience for our subscribers.</subfield></datafield><datafield tag="538" ind1=" " ind2=" "><subfield code="a">Mode of access: World Wide Web.</subfield></datafield><datafield tag="630" ind1="2" ind2="0"><subfield code="a">Hulu</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Consumer satisfaction</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Streaming technology (Telecommunications)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Big data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Consommateurs ; Satisfaction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">En continu (Télécommunications)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Données volumineuses</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electronic videos</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huff, Herbie</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="710" ind1="2" ind2=" "><subfield code="a">Safari, an O'Reilly Media Company.</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/00000ZXK1SDIF0BE/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="935" ind1=" " ind2=" "><subfield code="c">vide</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-054866413 |
| illustrated | Not Illustrated |
| indexdate | 2025-04-10T09:36:53Z |
| institution | BVB |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (1 video file, aSeitenSeitenroximately 29 min.) |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Data Science Salon |
| record_format | marc |
| spelling | Beley, Catherine VerfasserIn aut Hulu and Hang Understanding Viewer Sessions with Big Data Beley, Catherine 1st edition. [Erscheinungsort nicht ermittelbar] Data Science Salon 2019 Boston, MA Safari. 1 Online-Ressource (1 video file, aSeitenSeitenroximately 29 min.) zweidimensionales bewegtes Bild tdi rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; Title from title screen (viewed September 10, 2019) Everyday, Hulu ingests 100 terabytes of user level app interaction data. This session data is the closest touchpoint we have to subscribers' experience in our product short of joining them on their couch in their living rooms. Making meaning out of session data is a non-trivial effort across data instrumentation, engineering, analytics, and data science teams. In this talk, you will get an inside look at how we are tackling this monumental project at Hulu: from product design, to generating insights, to building predictive models - all to create the most personalized and engaging streaming experience for our subscribers. Mode of access: World Wide Web. Hulu Consumer satisfaction Streaming technology (Telecommunications) Big data Consommateurs ; Satisfaction En continu (Télécommunications) Données volumineuses Electronic videos Huff, Herbie VerfasserIn aut Safari, an O'Reilly Media Company. MitwirkendeR ctb |
| spellingShingle | Beley, Catherine Huff, Herbie Hulu and Hang Understanding Viewer Sessions with Big Data Hulu Consumer satisfaction Streaming technology (Telecommunications) Big data Consommateurs ; Satisfaction En continu (Télécommunications) Données volumineuses Electronic videos |
| title | Hulu and Hang Understanding Viewer Sessions with Big Data |
| title_auth | Hulu and Hang Understanding Viewer Sessions with Big Data |
| title_exact_search | Hulu and Hang Understanding Viewer Sessions with Big Data |
| title_full | Hulu and Hang Understanding Viewer Sessions with Big Data Beley, Catherine |
| title_fullStr | Hulu and Hang Understanding Viewer Sessions with Big Data Beley, Catherine |
| title_full_unstemmed | Hulu and Hang Understanding Viewer Sessions with Big Data Beley, Catherine |
| title_short | Hulu and Hang |
| title_sort | hulu and hang understanding viewer sessions with big data |
| title_sub | Understanding Viewer Sessions with Big Data |
| topic | Hulu Consumer satisfaction Streaming technology (Telecommunications) Big data Consommateurs ; Satisfaction En continu (Télécommunications) Données volumineuses Electronic videos |
| topic_facet | Hulu Consumer satisfaction Streaming technology (Telecommunications) Big data Consommateurs ; Satisfaction En continu (Télécommunications) Données volumineuses Electronic videos |
| work_keys_str_mv | AT beleycatherine huluandhangunderstandingviewersessionswithbigdata AT huffherbie huluandhangunderstandingviewersessionswithbigdata AT safarianoreillymediacompany huluandhangunderstandingviewersessionswithbigdata |