Case Study:Report from the Front Lines of Digital

Asset Management at CNN

Kathy Christensen

CNN News Archives

August 2001

2

CNN Background

• Multiple products: CNN, Headline News, CNN International, CNN.com et al, CNN/SI, CNNfn, CNN en Espanol, Airport Network, Inflight

• CNN Library as central resource

– Information research

– Archive

– Footage licensing

3

What’s in the CNN archive?• Type of material

– 10%: programs (Larry King, Crossfire, etc)

– 90% is raw footage & edited cut items (pkgs, sots, vo’s)

• Volume

– 150,000+ hours of footage in Atlanta plus additional footage in bureaus

– 1,000,000+items in Atlanta central catalog plus 600,000 across bureau catalogs

• Growth

– 2000 items archived per week in Atlanta culled from many times more incoming items

• 1/3 of items per day are cut (3 hrs)

• 2/3 of items per day are raw (90 hrs)

– 30,000 hours archived in 2000

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Who are the archive clients?

• CNN

– daily news - TV and Interactive

– documentary - TV and Interactive

– other (Sales, Marketing, PR, Legal, etc)

• AOL-TW companies (TNT, TBS, Warner Bros)

• External customers (Imagesource clients)

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The “Archive Project” (aka core of CNN’s digital future)

• Purpose– Preserve assets

– Extend usage of assets

– Create efficiencies

– Facilitate new business opportunities

– Create media management framework for the digital CNN

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Pre-Digital Scenario

Tape Library

Acquis ition ProductionRece iv ingContribu tion

D istribu tionRece iv ing

Rece iv ing

Production

Production

D istribu tion

D istribu tion

F ie ldProduction

S tud ioProduction

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Digital Scenario

Global ContentManagem ent andStorage System

Production Production

D istribu tion

HDTVD istribu tion

EnhancedD istribu tion

In teractiveD istribu tion

ProductionProduction

Production

Acquis itionEditing

Contribu tion

F ie ldProduction

S tud ioProduction

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• System goals and challenges

– Multiple resolutions captured simultaneously - to serve broadcast, edit and Internet

– Generate as much meaningful cataloging data automatically as possible - technology continuing to improve

– Support the necessary human cataloging with powerful tools

– Support retrieval needs of diverse user communities

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• Our Approach– Assemble a diverse internal team with multidisciplinary

expertise

• R&D, Engineering, IT, Library Science, Users

– Co-developers with Sony and IBM

• Key Principles– Custom solution not desired

– Focus on interoperability and standards

– Phased development

• get started and build on it

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Users drive cataloging & search requirements• Production usually demands video of versus stories about

– Automatically captured narrative track excellent for finding “about” but often misses the “of”-- what do we see in the footage?

– Special challenge of raw video -- b-roll often has no track to capture

• High-pressure, fast turn-around, 24-hour environment requires highly precise results, extremely quickly

• Long-term documentary production can tolerate more browsing but still requires reliably comprehensive retrieval

• News domain requires reliance on accuracy of editorial metadata - bad data and inadequate search systems equal journalistic problems

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Enablers of accuracy, precision, speed, thoroughness

• Controlled vs Free-form Data Entry - build data entry aids which support consistent entry

• Adequate size for keyword and video description fields

• Controlled classification terms with a mechanism for dynamically updating the classifications

• Fielded Tags for

– “best of” video

– about but not seen

– natural sound

• Flexibility in search approaches - free-text, controlled vocabulary, field-specific, user control over precision vs fuzziness, user control over tracks to include, user control over weighting and display of results

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Technology strengths supplement human weaknesses

• Automatic capture of closed-caption text improves retrieval of small, specific portions of programming about something -- a viewer need which is not easily met now.

• Voice-to-text transcription even at 60% accuracy fills a not-easily met need to find specific soundbites in raw speeches, interviews, hearings, etc.

• Video to video matching supports identification of permutations of the same video piece across the catalog

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Technology strengths supplements human strengths

• Making sense of images, putting them into editorial context, and attaching words so they may be retrieved

– Automatic scene change detection facilitates speedy review of item by human cataloger

– Face recognition software may not know who a particular face is, but can know that the video contains a face which a human can then identify

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Technology strengths also supplement technology weaknesses

• Speech-to-text weakness - some of the data most likely to be search on… names of people, companies, places

– Phonetic-based search strengths can cover speech-to-text search weakness

• Phonetic track useful for searching but doesn’t provide textual cataloging data

– Speech-to-text transcription useful as representation of the content of the asset

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Food for thought …

• Responsibilities

– to the parent company

– to the user communities

– to the rightsholders

– to posterity???

• This means thinking about

– Physical integrity of the content (quality, lossless conversions, standards, migration)

– Intellectual integrity of content…ethics