SlideShare ist ein Scribd-Unternehmen logo
1 von 32
1
Media fragment
re-mixing and playout
Lyndon Nixon
MODUL University Vienna
lyndon.nixon@modul.ac.at
RE-USING MEDIA ON THE WEB
WWW2014 Tutorial, Seoul, S Korea, April 8 2014
06.01.14 Slide 2 of 32
3. Media fragment re-mixing and
playout
• Storing the data: repositories
• Querying the data: retrieval
• Re-mixing the media: semantics
• Play out the media: clients
2
06.01.14 Slide 3 of 32
Repositories
06.01.14 Slide 4 of 32
Storing your RDF data
Like any data, RDF needs to be stored. It can be serialised to different formats
like XML and JSON, but this is not the same as the underlying data model.
• RDF is a GRAPH, XML and JSON are TREEs -> the same RDF data can be
serialised in different XML and JSON syntax
• RDF datatypes have different semantics than XML and JSON datatypes
In other words: if you want to persist and manipulate your data as RDF, you
should use a RDF specific solution.
06.01.14 Slide 5 of 32
RDF Stores
A RDF Store (or RDF repository, or triple store) is a database specialized for
RDF triples.
•Can ingest RDF in different serialisations
•Supports a RDF specific query language such as SPARQL
•May support RDF insert/delete (a.k.a. SPARQL UPDATE)
•Can support inferencing over RDF data
Different implementations, can be broadly categorized into:
•In-memory: Triples are stored in main memory (usually fastest)
•Native: Persistence is provided by using a custom DB, where the database
structure is optimised to the RDF model (most commonly used)
•Non-native: Persistence is provided by using a third party relational DBMS
(e.g. mySQL or postgres) (may be better if there are many updates or a need
for good concurrency control)
06.01.14 Slide 6 of 32
RDF Store Overview
Good overview at
http://www.w3.org/
2001/sw/wiki/Categ
ory:Triple_Store
(47 listed stores)
Scalability:
http://www.w3.org/
wiki/LargeTripleSto
res lists quoted
large scale
deployments of
stores (up to 1+
trillion RDF triples)
Comparison from 9/2012 at http://www.garshol.priv.no/blog/231.html
06.01.14 Slide 7 of 32
OpenRDF Sesame
Sesame is an open source RDF framework
for RDF storage, query and inference.
•http://www.openrdf.org
•Implemented in Java
•Supports in-memory, native and non-native
stores.
Features
•Lightweight yet powerful Java API
•SPARQL query language support (including
SPARQL UPDATE)
•Highly scalable
•Can support inference via a RDF Schema
reasoner
•Transactionality
•Context (a.k.a. Named Graphs)
RDF Model
Rio
SAIL API
SAIL Query Model
SeRQL SPARQL
Repository Access API
HTTP Serverapplication
application
HTTP / SPARQL protocol
06.01.14 Slide 8 of 32
Insert RDF into Sesame
The central concept in Sesame is the repository. You add and query data by
repository.
06.01.14 Slide 9 of 32
Using context
You can distinguish data sets in
one repository by using context
This is an additional URI attached to
each triple you insert. (thus also
referred to as quads)
You can now query or update only on
triples within one named context (in
RDF, this is called a named graph)
Context is useful for provenance
tracking and for versioning
If a query does not specify a context,
the default context is used. It can be
exclusive (only triples without a
context) or inclusive (all triples in all
contexts or none)
06.01.14 Slide 10 of 32
Browse RDF in Sesame
06.01.14 Slide 11 of 32
Retrieval
06.01.14 Slide 12 of 32
SPARQL RDF query language
RDF data is a „labeled, directed graph“
without any fixed vocabulary (this is defined
in RDF Schema). A query language for RDF
needs to support this data model:
•Tranversing paths in a RDF graph
•Being independent of any defined
vocabulary
•Able to query over the data or the schema
SPARQL („sparkle“) is a W3C standard
supported in most RDF tools
•SELECT... WHERE.... constructions like in
SQL
•Path expressions
•Additional keywords for more query
expressiveness
06.01.14 Slide 13 of 32
Examples: triple patterns
The basic idea in SPARQL is to match graph patterns against RDF graphs.To
understand graph patterns we must first define triple patterns:
• A triple pattern is similar to a triple (in RDF) but with one or more variables in
the place of a RDF resource (URI or literal)
Triple: dbpedia:Lou_Reed foaf:givenName „Lewis Allen Reed“ .
Triple pattern: dbpedia:Lou_Reed foaf:givenName ?name .
?name is the variable. If a RDF graph with the above triple is queried with the
below triple pattern, then in the SPARQL results the variable ?name would be
‚bound‘ to the value „Lewis Allen Reed“.
• A SPARQL query result is a set of bindings for the variables appearing in the
SELECT clause, based on matching RDF resources to variables in triple
patterns in the WHERE clause. .
06.01.14 Slide 14 of 32
Examples: conjunctive and
disjunctive query
In graph patterns, several triple patterns are listed within braces { ... } and
these are interpreted conjunctively:
Ex: { ?what tech:noOfWheels „4“ .
?what tech:minSpeed „180“ . }
The variable ?what will only be bound to resources which BOTH have 4 wheels
AND a minimum speed of 180.
You can also join results from distinct graph patterns using the UNION
keyword. Note that result sets from graph patterns and from UNIONs are
different, since UNION works disjunctively:
Ex: { ?what tech:noOfWheels „4“ . }
UNION { ?what tech:minSpeed „180“ . }
06.01.14 Slide 15 of 32
Examples: FILTER, ORDER BY
SPARQL has many other keywords. FILTER restricts variable bindings returned
in query results to those for which the filter expression evaluates to true. A filter
applies to solutions over the entire graph pattern it is contained in.
Ex: { ?what tech:noOfWheels „4“ .
?what tech:minSpeed ?speed .
FILTER ( ?speed > 170 ) }
The ORDER BY keyword determines the sequence of query results returned
according to a sort on the referenced variable‘s bindings, ascending by default:
Ex: { ?what tech:minSpeed ?speed . }
ORDER BY ?speed
Or ORDER BY DESC(?speed)
06.01.14 Slide 16 of 32
SPARQL in Sesame
06.01.14 Slide 17 of 32
Query for media fragments:
SPARQL-MM
Research work in progress: extending SPARQL to Media Fragments by adding
spatio-temporal filter and aggregration functions.
Relation Function Aggregation Function
Spatial mm:rightBeside mm:spatialIntersection
mm:spatialOverlaps mm:spatialBoundingBox
… …
Temporal mm:after mm:temporalIntersection
mm:temoralOverlaps mm:temporalIntermediate
… …
Combined mm:overlaps mm:boundingBox
mm:contains mm:intersection
Courtesy Thomas Kurz (Salzburg Research) & MICO EU project
http://demos.mico-project.eu/sparql-mm/sparql-mm/demo/index.html
06.01.14 Slide 18 of 32
Semantics
06.01.14 Slide 19 of 32
Semantic search?
Broadly, any search paradigm which makes use of machine understanding of
the data being queried.
Search term „Presidents of the United States“
CLASSICAL SEARCH
Return all media with the
string „Presidents of the
United States“ in their title or
description
SEMANTIC SEARCH
Return all media with an occurance
of an instance of the type „Presidents
of the United States“ in their
metadata, e.g. George W Bush, Bill
Clinton, Barack Obama ...
06.01.14 Slide 20 of 32
Search by types or categories
The set of „Presidents of the United States“
The category of „Presidents of the United States“
World Leaders
 Presidents
 Presidents of the United States
 Bill Clinton
 Barack Obama
 George W Bush
Barack Obama
George W Bush
Bill Clinton
06.01.14 Slide 21 of 32
Search by paths in the graph
RDF can be more expressive through defining properties with specific
semantics; semantic search can be more powerful by using these semantics
However, independent of a RDF vocabulary, shared graph properties can be an
indicator of resource similarity & used in domain-independent semantic search
?
? < 50
England
Age
BornIn
?v
?
?
?p
?p
06.01.14 Slide 22 of 32
Search with Linked Data
Linked Data: RDF metadata published on the Web, with information about each
resource discoverable via its unique URI. Machines can transverse the RDF
graph by looking up each URI in turn.
DBPedia: an extraction of the knowledge in Wikipedia (esp. the info boxes) as
RDF and published in its own namespace (http://dbpedia.org)
Each Wikipedia article of the form http://en.wikipedia.org/wiki/X has a DBPedia
resource at the URI http://dbpedia.org/resource/X
Has its own ontology which defines resource properties and classes, also
maps the Wikipedia categories using the dcterms:subject property.
Advantages: Linked Data URIs allow an unambiguous reference to information
about a concept, that information carries the authority of the data provider, all
applications have the same understanding of the concept
Disadvantages: information at the URI is not under your control, sometimes
messy/dirty (the case with DBPedia)  use local copies, extend with own
metadata
06.01.14 Slide 23 of 32
SPARQL endpoints and the
SERVICE keyword
Linked Data sources usually provide a SPARQL endpoint for their datasets
• A SPARQL endpoint is a SPARQL query processing service that supports the
SPARQL protocol (http://w3.org/TR/rdf-sparql-protocol): a definition of how to
send a SPARQL query to a Web Service and receive a query result.
e.g. DBPedia.org has a SPARQL endpoint at http://dbpedia.org/sparql
See a fuller list at http://esw.w3.org/topic/SparqlEndpoints
SPARQL 1.1 includes the SERVICE keyword to include queries to SPARQL
endpoints within a SPARQL query addressing a different repository.
• With this in Sesame, you can SPARQL query a local repository BUT include
bindings from external datasets within the query
Ex: SERVICE <http://dbpedia.org/sparql>
{ ?resource dcterms:subject . ?subject .
?related dcterms:subject ?subject . }
06.01.14 Slide 24 of 32
SPARQL for semantic search
06.01.14 Slide 25 of 32
Clients
06.01.14 Slide 26 of 32
What is a Media Fragment
Player?
Regular media players playback
full media assets, only the
consumer may seek within the
media.
A media fragment player supports
playback of (spatial and/or
temporal) media fragments in
place of the full media asset, the
customer doesn‘t have to do
anything.
We mean a player which can
correctly interpret a media
fragment as per the W3C Media
Fragment URI specification.
06.01.14 Slide 27 of 32
Media Fragments – Web
browser support
• Mozilla Firefox, since version 9
• Apple Safari, since Jan 2012
• Google Chrome, since Jan 2012
Focus on support for the temporal dimension in the HTML5
video player.
06.01.14 Slide 28 of 32
Media Fragments –
Javascript libraries
• mediafragment.js:
https://github.com/tomayac/Media-Fragments-URI
• xywh.js: https://github.com/tomayac/xywh.js
Focus on implementing the spatial and clock dimensions
within browsers.
06.01.14 Slide 29 of 32
Media Fragments –
SyNote player
http://smfplayer.synote.org/smfplayer/
• Cross-browser/device solution for Media Fragment playback via JavaScript
• Supports Media Fragment playback from online video platforms such as
YouTube and DailyMotion
• Wider video format support than native HTML5 (e.g. also Flash or Windows
Media)
06.01.14 Slide 30 of 32
What about Media
Fragment servers?
The advantage of media fragment playback should be also that ONLY the
content of the media fragment needs to be provided by the server to the client.
(in reality, Web servers are still serving the whole media asset, the fragment is
determined on the client)
Implementing Media Fragment support on the server side requires new
implementations of Web (media) servers... Ninsuma Media Server is a
prototypical demonstration of how a server should function with Media
Fragments.
http://ninsuna.elis.ugent.be/MediaFragmentsServer
06.01.14 Slide 31 of 32
Our Media Fragment Jukebox
http://www.mediamixer.eu/jukebox/ox/
32
Wrap up

Weitere ähnliche Inhalte

Was ist angesagt?

Resource description framework
Resource description frameworkResource description framework
Resource description frameworkhozifa1010
 
Hack U Barcelona 2011
Hack U Barcelona 2011Hack U Barcelona 2011
Hack U Barcelona 2011Peter Mika
 
Introduction To RDF and RDFS
Introduction To RDF and RDFSIntroduction To RDF and RDFS
Introduction To RDF and RDFSNilesh Wagmare
 
Querying Linked Data on Android
Querying Linked Data on AndroidQuerying Linked Data on Android
Querying Linked Data on AndroidEUCLID project
 
Sparql a simple knowledge query
Sparql  a simple knowledge querySparql  a simple knowledge query
Sparql a simple knowledge queryStanley Wang
 
Jarrar: SPARQL - RDF Query Language
Jarrar: SPARQL - RDF Query LanguageJarrar: SPARQL - RDF Query Language
Jarrar: SPARQL - RDF Query LanguageMustafa Jarrar
 
Linked data HHS 2015
Linked data HHS 2015Linked data HHS 2015
Linked data HHS 2015Cason Snow
 
Publishing data on the Semantic Web
Publishing data on the Semantic WebPublishing data on the Semantic Web
Publishing data on the Semantic WebPeter Mika
 
Year of the Monkey: Lessons from the first year of SearchMonkey
Year of the Monkey: Lessons from the first year of SearchMonkeyYear of the Monkey: Lessons from the first year of SearchMonkey
Year of the Monkey: Lessons from the first year of SearchMonkeyPeter Mika
 
SWT Lecture Session 9 - RDB2RDF direct mapping
SWT Lecture Session 9 - RDB2RDF direct mappingSWT Lecture Session 9 - RDB2RDF direct mapping
SWT Lecture Session 9 - RDB2RDF direct mappingMariano Rodriguez-Muro
 
Semantic Search Summer School2009
Semantic Search Summer School2009Semantic Search Summer School2009
Semantic Search Summer School2009Peter Mika
 
Introduction to linked data and the semantic web
Introduction to linked data and the semantic webIntroduction to linked data and the semantic web
Introduction to linked data and the semantic webDave Reynolds
 
Data Integration And Visualization
Data Integration And VisualizationData Integration And Visualization
Data Integration And VisualizationIvan Ermilov
 
Understanding RDF: the Resource Description Framework in Context (1999)
Understanding RDF: the Resource Description Framework in Context  (1999)Understanding RDF: the Resource Description Framework in Context  (1999)
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
 
The Semantic Web #9 - Web Ontology Language (OWL)
The Semantic Web #9 - Web Ontology Language (OWL)The Semantic Web #9 - Web Ontology Language (OWL)
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
 

Was ist angesagt? (20)

Resource description framework
Resource description frameworkResource description framework
Resource description framework
 
Hack U Barcelona 2011
Hack U Barcelona 2011Hack U Barcelona 2011
Hack U Barcelona 2011
 
Introduction to RDF
Introduction to RDFIntroduction to RDF
Introduction to RDF
 
Introduction To RDF and RDFS
Introduction To RDF and RDFSIntroduction To RDF and RDFS
Introduction To RDF and RDFS
 
Querying Linked Data on Android
Querying Linked Data on AndroidQuerying Linked Data on Android
Querying Linked Data on Android
 
Sparql a simple knowledge query
Sparql  a simple knowledge querySparql  a simple knowledge query
Sparql a simple knowledge query
 
Introduction to RDF Data Model
Introduction to RDF Data ModelIntroduction to RDF Data Model
Introduction to RDF Data Model
 
Jarrar: SPARQL - RDF Query Language
Jarrar: SPARQL - RDF Query LanguageJarrar: SPARQL - RDF Query Language
Jarrar: SPARQL - RDF Query Language
 
Linked data HHS 2015
Linked data HHS 2015Linked data HHS 2015
Linked data HHS 2015
 
Linked Open Data
Linked Open DataLinked Open Data
Linked Open Data
 
Publishing data on the Semantic Web
Publishing data on the Semantic WebPublishing data on the Semantic Web
Publishing data on the Semantic Web
 
Year of the Monkey: Lessons from the first year of SearchMonkey
Year of the Monkey: Lessons from the first year of SearchMonkeyYear of the Monkey: Lessons from the first year of SearchMonkey
Year of the Monkey: Lessons from the first year of SearchMonkey
 
SWT Lecture Session 9 - RDB2RDF direct mapping
SWT Lecture Session 9 - RDB2RDF direct mappingSWT Lecture Session 9 - RDB2RDF direct mapping
SWT Lecture Session 9 - RDB2RDF direct mapping
 
Semantic Search Summer School2009
Semantic Search Summer School2009Semantic Search Summer School2009
Semantic Search Summer School2009
 
Introduction to linked data and the semantic web
Introduction to linked data and the semantic webIntroduction to linked data and the semantic web
Introduction to linked data and the semantic web
 
GraphDB
GraphDBGraphDB
GraphDB
 
Data Integration And Visualization
Data Integration And VisualizationData Integration And Visualization
Data Integration And Visualization
 
Understanding RDF: the Resource Description Framework in Context (1999)
Understanding RDF: the Resource Description Framework in Context  (1999)Understanding RDF: the Resource Description Framework in Context  (1999)
Understanding RDF: the Resource Description Framework in Context (1999)
 
The Semantic Web #9 - Web Ontology Language (OWL)
The Semantic Web #9 - Web Ontology Language (OWL)The Semantic Web #9 - Web Ontology Language (OWL)
The Semantic Web #9 - Web Ontology Language (OWL)
 
SWT Lecture Session 11 - R2RML part 2
SWT Lecture Session 11 - R2RML part 2SWT Lecture Session 11 - R2RML part 2
SWT Lecture Session 11 - R2RML part 2
 

Ähnlich wie Re-using Media on the Web: Media fragment re-mixing and playout

First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
First Steps in Semantic Data Modelling and Search & Analytics in the CloudFirst Steps in Semantic Data Modelling and Search & Analytics in the Cloud
First Steps in Semantic Data Modelling and Search & Analytics in the CloudOntotext
 
Consuming Linked Data 4/5 Semtech2011
Consuming Linked Data 4/5 Semtech2011Consuming Linked Data 4/5 Semtech2011
Consuming Linked Data 4/5 Semtech2011Juan Sequeda
 
Deploying PHP applications using Virtuoso as Application Server
Deploying PHP applications using Virtuoso as Application ServerDeploying PHP applications using Virtuoso as Application Server
Deploying PHP applications using Virtuoso as Application Serverwebhostingguy
 
Introduction to metadata cleansing using SPARQL update queries
Introduction to metadata cleansing using SPARQL update queriesIntroduction to metadata cleansing using SPARQL update queries
Introduction to metadata cleansing using SPARQL update queriesEuropean Commission
 
SPARQL in the Semantic Web
SPARQL in the Semantic WebSPARQL in the Semantic Web
SPARQL in the Semantic WebJan Beeck
 
RDFa Introductory Course Session 3/4 Why RDFa
RDFa Introductory Course Session 3/4 Why RDFaRDFa Introductory Course Session 3/4 Why RDFa
RDFa Introductory Course Session 3/4 Why RDFaPlatypus
 
RDFa Semantic Web
RDFa Semantic WebRDFa Semantic Web
RDFa Semantic WebRob Paok
 
Wed roman tut_open_datapub
Wed roman tut_open_datapubWed roman tut_open_datapub
Wed roman tut_open_datapubeswcsummerschool
 
CSHALS 2010 W3C Semanic Web Tutorial
CSHALS 2010 W3C Semanic Web TutorialCSHALS 2010 W3C Semanic Web Tutorial
CSHALS 2010 W3C Semanic Web TutorialLeeFeigenbaum
 
Linked Data Tutorial
Linked Data TutorialLinked Data Tutorial
Linked Data TutorialSören Auer
 
Triplestore and SPARQL
Triplestore and SPARQLTriplestore and SPARQL
Triplestore and SPARQLLino Valdivia
 
Benchmarking RDF Metadata Representations: Reification, Singleton Property an...
Benchmarking RDF Metadata Representations: Reification, Singleton Property an...Benchmarking RDF Metadata Representations: Reification, Singleton Property an...
Benchmarking RDF Metadata Representations: Reification, Singleton Property an...Fabrizio Orlandi
 

Ähnlich wie Re-using Media on the Web: Media fragment re-mixing and playout (20)

First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
First Steps in Semantic Data Modelling and Search & Analytics in the CloudFirst Steps in Semantic Data Modelling and Search & Analytics in the Cloud
First Steps in Semantic Data Modelling and Search & Analytics in the Cloud
 
Consuming Linked Data 4/5 Semtech2011
Consuming Linked Data 4/5 Semtech2011Consuming Linked Data 4/5 Semtech2011
Consuming Linked Data 4/5 Semtech2011
 
Deploying PHP applications using Virtuoso as Application Server
Deploying PHP applications using Virtuoso as Application ServerDeploying PHP applications using Virtuoso as Application Server
Deploying PHP applications using Virtuoso as Application Server
 
Introduction to metadata cleansing using SPARQL update queries
Introduction to metadata cleansing using SPARQL update queriesIntroduction to metadata cleansing using SPARQL update queries
Introduction to metadata cleansing using SPARQL update queries
 
Semantic Web talk TEMPLATE
Semantic Web talk TEMPLATESemantic Web talk TEMPLATE
Semantic Web talk TEMPLATE
 
SPARQL in the Semantic Web
SPARQL in the Semantic WebSPARQL in the Semantic Web
SPARQL in the Semantic Web
 
Why rdfa
Why rdfaWhy rdfa
Why rdfa
 
RDFa Introductory Course Session 3/4 Why RDFa
RDFa Introductory Course Session 3/4 Why RDFaRDFa Introductory Course Session 3/4 Why RDFa
RDFa Introductory Course Session 3/4 Why RDFa
 
RDF and Java
RDF and JavaRDF and Java
RDF and Java
 
Analysis on semantic web layer cake entities
Analysis on semantic web layer cake entitiesAnalysis on semantic web layer cake entities
Analysis on semantic web layer cake entities
 
semanticweb
semanticwebsemanticweb
semanticweb
 
Longwell final ppt
Longwell final pptLongwell final ppt
Longwell final ppt
 
RDFa Semantic Web
RDFa Semantic WebRDFa Semantic Web
RDFa Semantic Web
 
Linked data and voyager
Linked data and voyagerLinked data and voyager
Linked data and voyager
 
Wed roman tut_open_datapub
Wed roman tut_open_datapubWed roman tut_open_datapub
Wed roman tut_open_datapub
 
.Net and Rdf APIs
.Net and Rdf APIs.Net and Rdf APIs
.Net and Rdf APIs
 
CSHALS 2010 W3C Semanic Web Tutorial
CSHALS 2010 W3C Semanic Web TutorialCSHALS 2010 W3C Semanic Web Tutorial
CSHALS 2010 W3C Semanic Web Tutorial
 
Linked Data Tutorial
Linked Data TutorialLinked Data Tutorial
Linked Data Tutorial
 
Triplestore and SPARQL
Triplestore and SPARQLTriplestore and SPARQL
Triplestore and SPARQL
 
Benchmarking RDF Metadata Representations: Reification, Singleton Property an...
Benchmarking RDF Metadata Representations: Reification, Singleton Property an...Benchmarking RDF Metadata Representations: Reification, Singleton Property an...
Benchmarking RDF Metadata Representations: Reification, Singleton Property an...
 

Mehr von MediaMixerCommunity

VideoLecturesMashup: using media fragments and semantic annotations to enable...
VideoLecturesMashup: using media fragments and semantic annotations to enable...VideoLecturesMashup: using media fragments and semantic annotations to enable...
VideoLecturesMashup: using media fragments and semantic annotations to enable...MediaMixerCommunity
 
Remixing Media on the Web: Media Fragment Specification and Semantics
Remixing Media on the Web: Media Fragment Specification and SemanticsRemixing Media on the Web: Media Fragment Specification and Semantics
Remixing Media on the Web: Media Fragment Specification and SemanticsMediaMixerCommunity
 
Re-using Media on the Web tutorial: Media Fragment Creation and Annotation
Re-using Media on the Web tutorial: Media Fragment Creation and AnnotationRe-using Media on the Web tutorial: Media Fragment Creation and Annotation
Re-using Media on the Web tutorial: Media Fragment Creation and AnnotationMediaMixerCommunity
 
Re-using Media on the Web Tutorial: Introduction and Examples
Re-using Media on the Web Tutorial: Introduction and ExamplesRe-using Media on the Web Tutorial: Introduction and Examples
Re-using Media on the Web Tutorial: Introduction and ExamplesMediaMixerCommunity
 
Semantic Multimedia Remixing - MediaEval 2013 Search and Hyperlinking Task
Semantic Multimedia Remixing - MediaEval 2013 Search and Hyperlinking TaskSemantic Multimedia Remixing - MediaEval 2013 Search and Hyperlinking Task
Semantic Multimedia Remixing - MediaEval 2013 Search and Hyperlinking TaskMediaMixerCommunity
 
Opening up audiovisual archives for media professionals and researchers
Opening up audiovisual archives for media professionals and researchersOpening up audiovisual archives for media professionals and researchers
Opening up audiovisual archives for media professionals and researchersMediaMixerCommunity
 
The Sensor Web - New Opportunities for MediaMixing
The Sensor Web - New Opportunities for MediaMixingThe Sensor Web - New Opportunities for MediaMixing
The Sensor Web - New Opportunities for MediaMixingMediaMixerCommunity
 
Building a linked data based content discovery service for the RTÉ Archives
Building a linked data based content discovery service for the RTÉ ArchivesBuilding a linked data based content discovery service for the RTÉ Archives
Building a linked data based content discovery service for the RTÉ ArchivesMediaMixerCommunity
 
Media Mixing in the broadcast TV industry
Media Mixing in the broadcast TV industryMedia Mixing in the broadcast TV industry
Media Mixing in the broadcast TV industryMediaMixerCommunity
 
Building a linked data based content discovery service for the RTÉ Archives
Building a linked data based content discovery service for the RTÉ ArchivesBuilding a linked data based content discovery service for the RTÉ Archives
Building a linked data based content discovery service for the RTÉ ArchivesMediaMixerCommunity
 
Semantic technologies for copyright management
Semantic technologies for copyright managementSemantic technologies for copyright management
Semantic technologies for copyright managementMediaMixerCommunity
 
Tell me why! ain't nothin' but a mistake describing media item differences w...
Tell me why! ain't nothin' but a mistake  describing media item differences w...Tell me why! ain't nothin' but a mistake  describing media item differences w...
Tell me why! ain't nothin' but a mistake describing media item differences w...MediaMixerCommunity
 
A feature analysis based fragment remix instrument
A feature analysis based fragment remix instrumentA feature analysis based fragment remix instrument
A feature analysis based fragment remix instrumentMediaMixerCommunity
 
Video concept detection by learning from web images
Video concept detection by learning from web imagesVideo concept detection by learning from web images
Video concept detection by learning from web imagesMediaMixerCommunity
 
Fast object re detection and localization in video for spatio-temporal fragme...
Fast object re detection and localization in video for spatio-temporal fragme...Fast object re detection and localization in video for spatio-temporal fragme...
Fast object re detection and localization in video for spatio-temporal fragme...MediaMixerCommunity
 
Example-Based Remixing of Multimedia Contents
Example-Based Remixing of Multimedia ContentsExample-Based Remixing of Multimedia Contents
Example-Based Remixing of Multimedia ContentsMediaMixerCommunity
 
Analysis of visual similarity in news videos with robust and memory efficient...
Analysis of visual similarity in news videos with robust and memory efficient...Analysis of visual similarity in news videos with robust and memory efficient...
Analysis of visual similarity in news videos with robust and memory efficient...MediaMixerCommunity
 
Intelligent tools-mitja-jermol-2013-bali-7 may2013
Intelligent tools-mitja-jermol-2013-bali-7 may2013Intelligent tools-mitja-jermol-2013-bali-7 may2013
Intelligent tools-mitja-jermol-2013-bali-7 may2013MediaMixerCommunity
 

Mehr von MediaMixerCommunity (19)

VideoLecturesMashup: using media fragments and semantic annotations to enable...
VideoLecturesMashup: using media fragments and semantic annotations to enable...VideoLecturesMashup: using media fragments and semantic annotations to enable...
VideoLecturesMashup: using media fragments and semantic annotations to enable...
 
Remixing Media on the Web: Media Fragment Specification and Semantics
Remixing Media on the Web: Media Fragment Specification and SemanticsRemixing Media on the Web: Media Fragment Specification and Semantics
Remixing Media on the Web: Media Fragment Specification and Semantics
 
Re-using Media on the Web tutorial: Media Fragment Creation and Annotation
Re-using Media on the Web tutorial: Media Fragment Creation and AnnotationRe-using Media on the Web tutorial: Media Fragment Creation and Annotation
Re-using Media on the Web tutorial: Media Fragment Creation and Annotation
 
Re-using Media on the Web Tutorial: Introduction and Examples
Re-using Media on the Web Tutorial: Introduction and ExamplesRe-using Media on the Web Tutorial: Introduction and Examples
Re-using Media on the Web Tutorial: Introduction and Examples
 
Semantic Multimedia Remixing - MediaEval 2013 Search and Hyperlinking Task
Semantic Multimedia Remixing - MediaEval 2013 Search and Hyperlinking TaskSemantic Multimedia Remixing - MediaEval 2013 Search and Hyperlinking Task
Semantic Multimedia Remixing - MediaEval 2013 Search and Hyperlinking Task
 
Opening up audiovisual archives for media professionals and researchers
Opening up audiovisual archives for media professionals and researchersOpening up audiovisual archives for media professionals and researchers
Opening up audiovisual archives for media professionals and researchers
 
The Sensor Web - New Opportunities for MediaMixing
The Sensor Web - New Opportunities for MediaMixingThe Sensor Web - New Opportunities for MediaMixing
The Sensor Web - New Opportunities for MediaMixing
 
Building a linked data based content discovery service for the RTÉ Archives
Building a linked data based content discovery service for the RTÉ ArchivesBuilding a linked data based content discovery service for the RTÉ Archives
Building a linked data based content discovery service for the RTÉ Archives
 
Media Mixing in the broadcast TV industry
Media Mixing in the broadcast TV industryMedia Mixing in the broadcast TV industry
Media Mixing in the broadcast TV industry
 
Building a linked data based content discovery service for the RTÉ Archives
Building a linked data based content discovery service for the RTÉ ArchivesBuilding a linked data based content discovery service for the RTÉ Archives
Building a linked data based content discovery service for the RTÉ Archives
 
Semantic multimedia remixing
Semantic multimedia remixingSemantic multimedia remixing
Semantic multimedia remixing
 
Semantic technologies for copyright management
Semantic technologies for copyright managementSemantic technologies for copyright management
Semantic technologies for copyright management
 
Tell me why! ain't nothin' but a mistake describing media item differences w...
Tell me why! ain't nothin' but a mistake  describing media item differences w...Tell me why! ain't nothin' but a mistake  describing media item differences w...
Tell me why! ain't nothin' but a mistake describing media item differences w...
 
A feature analysis based fragment remix instrument
A feature analysis based fragment remix instrumentA feature analysis based fragment remix instrument
A feature analysis based fragment remix instrument
 
Video concept detection by learning from web images
Video concept detection by learning from web imagesVideo concept detection by learning from web images
Video concept detection by learning from web images
 
Fast object re detection and localization in video for spatio-temporal fragme...
Fast object re detection and localization in video for spatio-temporal fragme...Fast object re detection and localization in video for spatio-temporal fragme...
Fast object re detection and localization in video for spatio-temporal fragme...
 
Example-Based Remixing of Multimedia Contents
Example-Based Remixing of Multimedia ContentsExample-Based Remixing of Multimedia Contents
Example-Based Remixing of Multimedia Contents
 
Analysis of visual similarity in news videos with robust and memory efficient...
Analysis of visual similarity in news videos with robust and memory efficient...Analysis of visual similarity in news videos with robust and memory efficient...
Analysis of visual similarity in news videos with robust and memory efficient...
 
Intelligent tools-mitja-jermol-2013-bali-7 may2013
Intelligent tools-mitja-jermol-2013-bali-7 may2013Intelligent tools-mitja-jermol-2013-bali-7 may2013
Intelligent tools-mitja-jermol-2013-bali-7 may2013
 

Kürzlich hochgeladen

ETHICAL HACKING dddddddddddddddfnandni.pptx
ETHICAL HACKING dddddddddddddddfnandni.pptxETHICAL HACKING dddddddddddddddfnandni.pptx
ETHICAL HACKING dddddddddddddddfnandni.pptxNIMMANAGANTI RAMAKRISHNA
 
Top 10 Interactive Website Design Trends in 2024.pptx
Top 10 Interactive Website Design Trends in 2024.pptxTop 10 Interactive Website Design Trends in 2024.pptx
Top 10 Interactive Website Design Trends in 2024.pptxDyna Gilbert
 
Film cover research (1).pptxsdasdasdasdasdasa
Film cover research (1).pptxsdasdasdasdasdasaFilm cover research (1).pptxsdasdasdasdasdasa
Film cover research (1).pptxsdasdasdasdasdasa494f574xmv
 
办理多伦多大学毕业证成绩单|购买加拿大UTSG文凭证书
办理多伦多大学毕业证成绩单|购买加拿大UTSG文凭证书办理多伦多大学毕业证成绩单|购买加拿大UTSG文凭证书
办理多伦多大学毕业证成绩单|购买加拿大UTSG文凭证书zdzoqco
 
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书rnrncn29
 
TRENDS Enabling and inhibiting dimensions.pptx
TRENDS Enabling and inhibiting dimensions.pptxTRENDS Enabling and inhibiting dimensions.pptx
TRENDS Enabling and inhibiting dimensions.pptxAndrieCagasanAkio
 
IP addressing and IPv6, presented by Paul Wilson at IETF 119
IP addressing and IPv6, presented by Paul Wilson at IETF 119IP addressing and IPv6, presented by Paul Wilson at IETF 119
IP addressing and IPv6, presented by Paul Wilson at IETF 119APNIC
 
Company Snapshot Theme for Business by Slidesgo.pptx
Company Snapshot Theme for Business by Slidesgo.pptxCompany Snapshot Theme for Business by Slidesgo.pptx
Company Snapshot Theme for Business by Slidesgo.pptxMario
 
SCM Symposium PPT Format Customer loyalty is predi
SCM Symposium PPT Format Customer loyalty is prediSCM Symposium PPT Format Customer loyalty is predi
SCM Symposium PPT Format Customer loyalty is predieusebiomeyer
 
Unidad 4 – Redes de ordenadores (en inglés).pptx
Unidad 4 – Redes de ordenadores (en inglés).pptxUnidad 4 – Redes de ordenadores (en inglés).pptx
Unidad 4 – Redes de ordenadores (en inglés).pptxmibuzondetrabajo
 
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书rnrncn29
 

Kürzlich hochgeladen (11)

ETHICAL HACKING dddddddddddddddfnandni.pptx
ETHICAL HACKING dddddddddddddddfnandni.pptxETHICAL HACKING dddddddddddddddfnandni.pptx
ETHICAL HACKING dddddddddddddddfnandni.pptx
 
Top 10 Interactive Website Design Trends in 2024.pptx
Top 10 Interactive Website Design Trends in 2024.pptxTop 10 Interactive Website Design Trends in 2024.pptx
Top 10 Interactive Website Design Trends in 2024.pptx
 
Film cover research (1).pptxsdasdasdasdasdasa
Film cover research (1).pptxsdasdasdasdasdasaFilm cover research (1).pptxsdasdasdasdasdasa
Film cover research (1).pptxsdasdasdasdasdasa
 
办理多伦多大学毕业证成绩单|购买加拿大UTSG文凭证书
办理多伦多大学毕业证成绩单|购买加拿大UTSG文凭证书办理多伦多大学毕业证成绩单|购买加拿大UTSG文凭证书
办理多伦多大学毕业证成绩单|购买加拿大UTSG文凭证书
 
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书
 
TRENDS Enabling and inhibiting dimensions.pptx
TRENDS Enabling and inhibiting dimensions.pptxTRENDS Enabling and inhibiting dimensions.pptx
TRENDS Enabling and inhibiting dimensions.pptx
 
IP addressing and IPv6, presented by Paul Wilson at IETF 119
IP addressing and IPv6, presented by Paul Wilson at IETF 119IP addressing and IPv6, presented by Paul Wilson at IETF 119
IP addressing and IPv6, presented by Paul Wilson at IETF 119
 
Company Snapshot Theme for Business by Slidesgo.pptx
Company Snapshot Theme for Business by Slidesgo.pptxCompany Snapshot Theme for Business by Slidesgo.pptx
Company Snapshot Theme for Business by Slidesgo.pptx
 
SCM Symposium PPT Format Customer loyalty is predi
SCM Symposium PPT Format Customer loyalty is prediSCM Symposium PPT Format Customer loyalty is predi
SCM Symposium PPT Format Customer loyalty is predi
 
Unidad 4 – Redes de ordenadores (en inglés).pptx
Unidad 4 – Redes de ordenadores (en inglés).pptxUnidad 4 – Redes de ordenadores (en inglés).pptx
Unidad 4 – Redes de ordenadores (en inglés).pptx
 
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书
 

Re-using Media on the Web: Media fragment re-mixing and playout

  • 1. 1 Media fragment re-mixing and playout Lyndon Nixon MODUL University Vienna lyndon.nixon@modul.ac.at RE-USING MEDIA ON THE WEB WWW2014 Tutorial, Seoul, S Korea, April 8 2014
  • 2. 06.01.14 Slide 2 of 32 3. Media fragment re-mixing and playout • Storing the data: repositories • Querying the data: retrieval • Re-mixing the media: semantics • Play out the media: clients 2
  • 3. 06.01.14 Slide 3 of 32 Repositories
  • 4. 06.01.14 Slide 4 of 32 Storing your RDF data Like any data, RDF needs to be stored. It can be serialised to different formats like XML and JSON, but this is not the same as the underlying data model. • RDF is a GRAPH, XML and JSON are TREEs -> the same RDF data can be serialised in different XML and JSON syntax • RDF datatypes have different semantics than XML and JSON datatypes In other words: if you want to persist and manipulate your data as RDF, you should use a RDF specific solution.
  • 5. 06.01.14 Slide 5 of 32 RDF Stores A RDF Store (or RDF repository, or triple store) is a database specialized for RDF triples. •Can ingest RDF in different serialisations •Supports a RDF specific query language such as SPARQL •May support RDF insert/delete (a.k.a. SPARQL UPDATE) •Can support inferencing over RDF data Different implementations, can be broadly categorized into: •In-memory: Triples are stored in main memory (usually fastest) •Native: Persistence is provided by using a custom DB, where the database structure is optimised to the RDF model (most commonly used) •Non-native: Persistence is provided by using a third party relational DBMS (e.g. mySQL or postgres) (may be better if there are many updates or a need for good concurrency control)
  • 6. 06.01.14 Slide 6 of 32 RDF Store Overview Good overview at http://www.w3.org/ 2001/sw/wiki/Categ ory:Triple_Store (47 listed stores) Scalability: http://www.w3.org/ wiki/LargeTripleSto res lists quoted large scale deployments of stores (up to 1+ trillion RDF triples) Comparison from 9/2012 at http://www.garshol.priv.no/blog/231.html
  • 7. 06.01.14 Slide 7 of 32 OpenRDF Sesame Sesame is an open source RDF framework for RDF storage, query and inference. •http://www.openrdf.org •Implemented in Java •Supports in-memory, native and non-native stores. Features •Lightweight yet powerful Java API •SPARQL query language support (including SPARQL UPDATE) •Highly scalable •Can support inference via a RDF Schema reasoner •Transactionality •Context (a.k.a. Named Graphs) RDF Model Rio SAIL API SAIL Query Model SeRQL SPARQL Repository Access API HTTP Serverapplication application HTTP / SPARQL protocol
  • 8. 06.01.14 Slide 8 of 32 Insert RDF into Sesame The central concept in Sesame is the repository. You add and query data by repository.
  • 9. 06.01.14 Slide 9 of 32 Using context You can distinguish data sets in one repository by using context This is an additional URI attached to each triple you insert. (thus also referred to as quads) You can now query or update only on triples within one named context (in RDF, this is called a named graph) Context is useful for provenance tracking and for versioning If a query does not specify a context, the default context is used. It can be exclusive (only triples without a context) or inclusive (all triples in all contexts or none)
  • 10. 06.01.14 Slide 10 of 32 Browse RDF in Sesame
  • 11. 06.01.14 Slide 11 of 32 Retrieval
  • 12. 06.01.14 Slide 12 of 32 SPARQL RDF query language RDF data is a „labeled, directed graph“ without any fixed vocabulary (this is defined in RDF Schema). A query language for RDF needs to support this data model: •Tranversing paths in a RDF graph •Being independent of any defined vocabulary •Able to query over the data or the schema SPARQL („sparkle“) is a W3C standard supported in most RDF tools •SELECT... WHERE.... constructions like in SQL •Path expressions •Additional keywords for more query expressiveness
  • 13. 06.01.14 Slide 13 of 32 Examples: triple patterns The basic idea in SPARQL is to match graph patterns against RDF graphs.To understand graph patterns we must first define triple patterns: • A triple pattern is similar to a triple (in RDF) but with one or more variables in the place of a RDF resource (URI or literal) Triple: dbpedia:Lou_Reed foaf:givenName „Lewis Allen Reed“ . Triple pattern: dbpedia:Lou_Reed foaf:givenName ?name . ?name is the variable. If a RDF graph with the above triple is queried with the below triple pattern, then in the SPARQL results the variable ?name would be ‚bound‘ to the value „Lewis Allen Reed“. • A SPARQL query result is a set of bindings for the variables appearing in the SELECT clause, based on matching RDF resources to variables in triple patterns in the WHERE clause. .
  • 14. 06.01.14 Slide 14 of 32 Examples: conjunctive and disjunctive query In graph patterns, several triple patterns are listed within braces { ... } and these are interpreted conjunctively: Ex: { ?what tech:noOfWheels „4“ . ?what tech:minSpeed „180“ . } The variable ?what will only be bound to resources which BOTH have 4 wheels AND a minimum speed of 180. You can also join results from distinct graph patterns using the UNION keyword. Note that result sets from graph patterns and from UNIONs are different, since UNION works disjunctively: Ex: { ?what tech:noOfWheels „4“ . } UNION { ?what tech:minSpeed „180“ . }
  • 15. 06.01.14 Slide 15 of 32 Examples: FILTER, ORDER BY SPARQL has many other keywords. FILTER restricts variable bindings returned in query results to those for which the filter expression evaluates to true. A filter applies to solutions over the entire graph pattern it is contained in. Ex: { ?what tech:noOfWheels „4“ . ?what tech:minSpeed ?speed . FILTER ( ?speed > 170 ) } The ORDER BY keyword determines the sequence of query results returned according to a sort on the referenced variable‘s bindings, ascending by default: Ex: { ?what tech:minSpeed ?speed . } ORDER BY ?speed Or ORDER BY DESC(?speed)
  • 16. 06.01.14 Slide 16 of 32 SPARQL in Sesame
  • 17. 06.01.14 Slide 17 of 32 Query for media fragments: SPARQL-MM Research work in progress: extending SPARQL to Media Fragments by adding spatio-temporal filter and aggregration functions. Relation Function Aggregation Function Spatial mm:rightBeside mm:spatialIntersection mm:spatialOverlaps mm:spatialBoundingBox … … Temporal mm:after mm:temporalIntersection mm:temoralOverlaps mm:temporalIntermediate … … Combined mm:overlaps mm:boundingBox mm:contains mm:intersection Courtesy Thomas Kurz (Salzburg Research) & MICO EU project http://demos.mico-project.eu/sparql-mm/sparql-mm/demo/index.html
  • 18. 06.01.14 Slide 18 of 32 Semantics
  • 19. 06.01.14 Slide 19 of 32 Semantic search? Broadly, any search paradigm which makes use of machine understanding of the data being queried. Search term „Presidents of the United States“ CLASSICAL SEARCH Return all media with the string „Presidents of the United States“ in their title or description SEMANTIC SEARCH Return all media with an occurance of an instance of the type „Presidents of the United States“ in their metadata, e.g. George W Bush, Bill Clinton, Barack Obama ...
  • 20. 06.01.14 Slide 20 of 32 Search by types or categories The set of „Presidents of the United States“ The category of „Presidents of the United States“ World Leaders  Presidents  Presidents of the United States  Bill Clinton  Barack Obama  George W Bush Barack Obama George W Bush Bill Clinton
  • 21. 06.01.14 Slide 21 of 32 Search by paths in the graph RDF can be more expressive through defining properties with specific semantics; semantic search can be more powerful by using these semantics However, independent of a RDF vocabulary, shared graph properties can be an indicator of resource similarity & used in domain-independent semantic search ? ? < 50 England Age BornIn ?v ? ? ?p ?p
  • 22. 06.01.14 Slide 22 of 32 Search with Linked Data Linked Data: RDF metadata published on the Web, with information about each resource discoverable via its unique URI. Machines can transverse the RDF graph by looking up each URI in turn. DBPedia: an extraction of the knowledge in Wikipedia (esp. the info boxes) as RDF and published in its own namespace (http://dbpedia.org) Each Wikipedia article of the form http://en.wikipedia.org/wiki/X has a DBPedia resource at the URI http://dbpedia.org/resource/X Has its own ontology which defines resource properties and classes, also maps the Wikipedia categories using the dcterms:subject property. Advantages: Linked Data URIs allow an unambiguous reference to information about a concept, that information carries the authority of the data provider, all applications have the same understanding of the concept Disadvantages: information at the URI is not under your control, sometimes messy/dirty (the case with DBPedia)  use local copies, extend with own metadata
  • 23. 06.01.14 Slide 23 of 32 SPARQL endpoints and the SERVICE keyword Linked Data sources usually provide a SPARQL endpoint for their datasets • A SPARQL endpoint is a SPARQL query processing service that supports the SPARQL protocol (http://w3.org/TR/rdf-sparql-protocol): a definition of how to send a SPARQL query to a Web Service and receive a query result. e.g. DBPedia.org has a SPARQL endpoint at http://dbpedia.org/sparql See a fuller list at http://esw.w3.org/topic/SparqlEndpoints SPARQL 1.1 includes the SERVICE keyword to include queries to SPARQL endpoints within a SPARQL query addressing a different repository. • With this in Sesame, you can SPARQL query a local repository BUT include bindings from external datasets within the query Ex: SERVICE <http://dbpedia.org/sparql> { ?resource dcterms:subject . ?subject . ?related dcterms:subject ?subject . }
  • 24. 06.01.14 Slide 24 of 32 SPARQL for semantic search
  • 25. 06.01.14 Slide 25 of 32 Clients
  • 26. 06.01.14 Slide 26 of 32 What is a Media Fragment Player? Regular media players playback full media assets, only the consumer may seek within the media. A media fragment player supports playback of (spatial and/or temporal) media fragments in place of the full media asset, the customer doesn‘t have to do anything. We mean a player which can correctly interpret a media fragment as per the W3C Media Fragment URI specification.
  • 27. 06.01.14 Slide 27 of 32 Media Fragments – Web browser support • Mozilla Firefox, since version 9 • Apple Safari, since Jan 2012 • Google Chrome, since Jan 2012 Focus on support for the temporal dimension in the HTML5 video player.
  • 28. 06.01.14 Slide 28 of 32 Media Fragments – Javascript libraries • mediafragment.js: https://github.com/tomayac/Media-Fragments-URI • xywh.js: https://github.com/tomayac/xywh.js Focus on implementing the spatial and clock dimensions within browsers.
  • 29. 06.01.14 Slide 29 of 32 Media Fragments – SyNote player http://smfplayer.synote.org/smfplayer/ • Cross-browser/device solution for Media Fragment playback via JavaScript • Supports Media Fragment playback from online video platforms such as YouTube and DailyMotion • Wider video format support than native HTML5 (e.g. also Flash or Windows Media)
  • 30. 06.01.14 Slide 30 of 32 What about Media Fragment servers? The advantage of media fragment playback should be also that ONLY the content of the media fragment needs to be provided by the server to the client. (in reality, Web servers are still serving the whole media asset, the fragment is determined on the client) Implementing Media Fragment support on the server side requires new implementations of Web (media) servers... Ninsuma Media Server is a prototypical demonstration of how a server should function with Media Fragments. http://ninsuna.elis.ugent.be/MediaFragmentsServer
  • 31. 06.01.14 Slide 31 of 32 Our Media Fragment Jukebox http://www.mediamixer.eu/jukebox/ox/