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Big Data, Big Media

THE NEW PARADIGM OF THE MULTIMEDIA CONTENT MANAGEMENT

Semtech top 10 startup of 2013 IBC Award for « Content management » 2013

IBC Award for technology « Who caught my eye looking for blue skies » of IBC 2013

@perfect__memory http://perfect-memory.com

Perfect Memory – The Team

2BIG DATA - BIG MEDIA

Perfect Memory – The eco-system

Registered in 2008, a Ltd with 245 K€ capital

Funded by SOFIMAC Partner a major investor in France

Deploying for Media, Media-Trade, Archivers and Big Companies

Expert in management, indexation, and of monetization of mass volumes of Multi Media

Owning a unique Middleware process transforming raw data into Knowledge


The context of Big Data

Big Data is a buzz word that hide different realties :

- Volume issue (data volume, media volume),

- Interoperability that serves the ubiquity of the content (anywhere, anytime, anybody),

- Diversity of sources (MAM, DB, internal, external, structured, unstructured).

Deals with volume, ubiquity and diversity

Le 09/10/2013 BIG DATA - BIG MEDIA 4

The context of Big Data

Volume is an old issue :

- Upstream: Require to solve the administration, the exploitation and the indexing of the content,

- Downstream: provide mapping and representation of the content.

A posteriori, analytics, Data mining

Health, Oil, Retail (see the the diaper & beer case)

Interoperability is a consequence of the raising of the Internet:

- Cooperation, communities, coworking,

- It requires standards (XML Schema, MPEG 4,7,21, MXF, OAIS, RDF.

Deals with volume, ubiquity and diversity

- It requires standards (XML Schema, MPEG 4,7,21, MXF, OAIS, RDF.

A priori, structuration of the content

Media

New movie workflow (production to distribution)

Diversity of sources:

- Structuration, meaning,

- Linked data.

A priori, knowledge processing

Media, industrie, Education

Web 3.0 paradigm (Semantic, LOD, Open Data)


Facts…

1.Multi media contents are growing massively

2.Media inventories are managed by heterogeneous systems

3. Indexation, if done, is mainly donemanually

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Media2000 2005 ∞∞

Media Asset Management Systems

time


Facts…

… generating deep archives issue ….

Main facts

Lost opportunities

… and management issue

Waste of time


Media Challenge : New needs


Media Challenge : new comers

The Media-brands


Summary of functional needs

During our conversations we have identified the needs to:

• Structure the content using opened and documented standards,

• Link, enrich & index the massive volumes of Contents,

• Browse inside the massive volumes of Contents,

• Manage the content all along its life cycle,

• Monetize & Value the content.

• Become autonomous in the administration of the knowledge and its infrastructure

• Being flexible in term of strategy of knowledge management

• Avoid starting from scratch


Summary of our solution

The semantic middleware is :

• Natively compliant to the main media standards (EBU Core, FIMS, OAIS,…)

• Providing a media mapping manager (multiple instances of items handling),

• A non intrusive, scalable and flexible platform,

• Self learning, opened to other modules and functionalities,

• Transferable platform


Semantic layer cakeFrom modeling to exploitation

User Interface

USAGE

360° Rendering

PUBLISHING

Inference rules

ENRICHMENT

Semantic Data

PRODUCTION, INGEST

Ontology & knowledge base

MODELING


Semantic valorizationWhy?

• From DATA to information

• Understand information and build theknowledgeknowledge

• Provide solutions to value the content.


Semantic valorization

Bring semantic to the media

Data Info

Knowledge

2 persons, face to face, smiling and laughing

Semantic

system

Data InfoKnowledge

2 persons, face to face, smiling and laughing

• Samuel L. Jackson (Person)

• Leonardo DiCaprio (Person)

• Thumbnail from “Django unchained”

• Quentin Tarantino behind the camera


Enhancement & Enrichment

From flat to rich content

ENTITY « Organisation »

URI: MMC#RTBF#6756593

Name : RTBF

ENTITY « Person »


Name : Barthe

First name: François

ENTITY « Person »

URI: MMC#RTBF#6753

Name : Zidane

First name: Zinedine

« Works

for »

« Talk

about »

Enrichment3

Enhancement

semantic

2Table « Person »

Id : #67554778

Name : Barthe

First name: François

Employer : RTBF

Description : > Worked on Zidane’s

bio.

1

ENTITY « Person »


Name : Barthe

First name : François

Description :

> Worked on Zidane’s bio.

ENTITY « Organisation »


Name : RTBF

« Work

for »

for »

semantic

Enrichment

semantic

3

Le 09/10/2013 15BIG DATA - BIG MEDIA

Inference

Perfect Memory’s data bases :

• Increasing of amount of information in time,

• Increasing of quality of links in time.

Preserve, enrich and sharing of knowledge.

Capitalisation of knowledge

Semantic

Inference

4

Semantic negentropic DB

News facts

Inference rules


Exploiting the knowledgeExploiting the knowledge

From Pr. Bachimont – University of Technology of Compiègne


Linked Open DataConsolidating the distributed knowledge

RTBF


Process managementOSB workers

InterOp-WindowA service on the OSB

1. Manager: Identification of request

2. Manager: Main process instantation

3. Manager: Sub process instantiation

4. Manager :Tasks instantiation

5. Manager: IOW calls

6. Guichets#1 : Execution of tasks and works

7. …

8. Guichets#n : Execution of tasks and works

Treatment

request

8. Guichets#n : Execution of tasks and works


Semantic PlayerRendering the semantic links

InterOp-Guichet

Player SémantiqueNetworkNetwork

OSB OSB

OSB OSB

Enhancement, Repurposing & Exploitation of

audiovisual contents.


An architecture scalable, distributedIntroducing the Semantic Middleware approach

SEMANTIC

PLAYER

(1) The Heart inludes the Knowledge base features, and the OAIS functionalities

(1)

(2)

YOUR

APPLICATION

(2) The BUS, 100% compliant to EBUCore, becomes the backboneof the middleware

(3) Any Bases ingested, or functionalitiesconnected via an InteOperability Windows (GIO) becomes a semantic ressource for the Middleware

(2)

(3)


Flexibility & scalability of the middleware

Control Enrichment

ExtractionExpressivity


RTBF Annotation & search interfaces

Features:

� Breakthrough user friendly interface for big data visualization

� Graphical browsing in big data content (media and metadata)


Radio France Tablet Interface

Connection:

� Building the contextualization of the display according to the Role and Skill of

the connected user.24Big Data - Big Media

The PROFILE : knowledge capitalization

YourYour Data StructureData Structure

YourYour Media LibraryMedia Library Linked Open DataLinked Open Data

YOURYOUR KNOWLEDGEKNOWLEDGE


The Middleware features …

• Automatic linking with external related contents,

BeforeBefore

After•

• Automatic knowledge validation,

• Cross-browsing in broadcasters’ MAMs.

After

Media Processing


The semantic middleware approach


BIG DATA – BIG MEDIA

THE NEW PARADIGM OF THE MULTIMEDIA CONTENT MANAGEMENT

Frédéric Colomina

Business Development

[email protected]

@perfect__memory

Steny Solitude

CEO

[email protected]

@perfect__memory

Semtech top 10 startup of 2013 IBC Award for « Content management » 2013

IBC Award for technology « Who caught my eye looking for blue skies » of IBC 2013

@perfect__memory http://perfect-memory.com

Organizadores Sponsor platinum

Sponsor Gold Con el apoyo de Socio tecnológico

Nicolas Moulard, Director de Actuonda

[email protected]

Tel : +34 699 248 200

@Radio_20 www.bigmediaconnect.es