Big Data Big Media the new paradigm of multimedia content management with Perfect Memory at Big Media by Actuonda

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 done manually
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Media
2000 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 cake
From modeling to exploitation
User Interface
USAGE
360° Rendering
PUBLISHING
Inference rules
ENRICHMENT
Semantic Data
PRODUCTION, INGEST
Ontology & knowledge base
MODELING

Semantic valorization
Why?
• From DATA to information
• Understand information and build the
knowledgeknowledge
• 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 Info
Knowledge
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 Data
Consolidating the distributed knowledge
RTBF

Process management
OSB workers
InterOp-Window
A 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 Player
Rendering the semantic links
InterOp-Guichet
Player SémantiqueNetworkNetwork
OSB OSB
OSB OSB
Enhancement, Repurposing & Exploitation of
audiovisual contents.

An architecture scalable, distributed
Introducing 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 backbone
of the middleware
(3) Any Bases ingested,
or functionalities
connected via an
InteOperability Windows
(GIO) becomes a
semantic ressource for
the Middleware
(2)
(3)

Flexibility & scalability of the middleware
Control Enrichment
Extraction
Expressivity

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
Frederic.colomina@perfect-memory.com
@perfect__memory
Steny Solitude
CEO
Steny.solitude@perfect-memory.com
@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
moulard@actuonda.com
Tel : +34 699 248 200
@Radio_20 www.bigmediaconnect.es

Big Data Big Media the new paradigm of multimedia content management with Perfect Memory at Big Media by Actuonda

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Big Data Big Media the new paradigm of multimedia content management with Perfect Memory at Big Media by Actuonda