About Multimedia Presentation Generation and Multimedia Metadata: From Synthesis to Analysis, and Back?

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ACM SIGMM Rising Stars Symposium The ACM SIGMM Rising Stars Symposium, inaugurated in 2015, will highlight plenary presentations of six selected rising SIGMM members on their vision and research achievements, and dialogs with senior members about the future of multimedia research. See: http://www.acmmm.org/2016/?page_id=706

Technology

Multimedia Presentation Generation
and Multimedia Metadata: From
Synthesis to Analysis, and Back?
Ansgar Scherp

ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 2
Multimedia Presentation Generation
Ansgar Scherp, Susanne Boll: Paving the Last Mile for Multi-Channel Multimedia
Presentation Generation. MMM 2005: 190-197 (Best Paper Award)
It is all about the media metadata
not the media content itself ...
• Dynamic generation
• Multi-channel output

ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 4
We Care So Much About Metadata!
• Really? So how can it be that we ...
… almost loose all metadata after assemle?
Semantics Barrier!

ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 5
Idea: Emergent Semantics
• Semantics that emerges from putting the single
pieces of media content together
• “Sum is more than just the set of its pieces”
• Wait a minute: how to describe the semantics?

ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 6
Example: Emergent Semantics
S. Boll, P. Sandhaus,
A. Scherp, U. Westermann:
Semantics, content, and
structure of many for the
creation of personal photo
albums. ACM Multimedia
2007: 641-650
• Rule-based
• Customizable
• Multi-channel

Slide 7ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu
Embedding Metadata in SMIL
<smil xmlns="http://w3.org/2006/SMIL30/...">
<head>
<!-- Metadata -->
<metadata id="meta-rdf"> </metadata>
<!-- layout -->
</head>
<body>
<!-- Presentation content -->
<seq id="mainPresentation">
<par xml:id=“achievements">...</par>
<par xml:id=“risks">...</par>
</seq>
</body>
</smil>
???

Slide 8ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu
Solution Approach
Formal modeling framework for
representing and integrating
multimedia metadata
Multimedia Metadata
Ontology (M3O)
Carsten Saathoff, Ansgar Scherp: Unlocking the semantics of multimedia
presentations in the web with the multimedia metadata ontology. WWW 2010: 831-840

Slide 9ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu
ACM Multimedia 2010, Firence, Italy

ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 10
Some Cool Semantics Analyses ...
Text
Cancer (18899-3)
Research (10436-6)
USA (17829-1)
…
Smart Photo Selection (CHI'14)
Gaze Analysis in Search (IUI'14)
Document Classification (KCAP'15)
(Nomination for Best Paper Award)
Text Extraction from Images
(DocEng'15)

ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 11
MOVING back to the roots?!
How to deal with the vast information
in research and innovation?
Generating cMOOCs
Personalized advise (log analyses)
Advanced search and retrieval
EU H2020 project since April 2016, budget: 3.5 M €, URL: http://moving-project.eu/

ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 12
Use Cases
• Research on business information
by public administrators
– Understand complex situations
– Increase compliance with current
laws and regulations
• Managing & mining research information
– Reduce information overload
– Assist in evaluating and selecting
relevant information resources

ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 13
Example: EconBiz's Guided Walk
• URL: http://www.econbiz.de/eb/gw/

ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 14
Thank you ...
... for giving me a good reason and the fun
to burrow in my own paper archive!
Contact me!
Email: a.scherp@zbw.eu
• Twitter: https://twitter.com/ansgarscherp
• Slideshare: http://de.slideshare.net/ascherp
• KD-Website:
http://www.zbw.eu/en/research/knowledge-discovery/
http://www.kd.informatik.uni-kiel.de/en/

Linked Open Data (LOD) is about publishing and interlinking data of different origin and purpose on the web. The Resource Description Framework (RDF) is used to describe data on the LOD cloud. In contrast to relational databases, RDF does not provide a fixed, pre-defined schema. Rather, RDF allows for flexibly modeling the data schema by attaching RDF types and properties to the entities. Our schema-level index called SchemEX allows for searching in large-scale RDF graph data. The index can be efficiently computed with reasonable accuracy over large-scale data sets with billions of RDF triples, the smallest information unit on the LOD cloud. SchemEX is highly needed as the size of the LOD cloud quickly increases. Due to the evolution of the LOD cloud, one observes frequent changes of the data. We show that also the data schema changes in terms of combinations of RDF types and properties. As changes cannot capture the dynamics of the LOD cloud, current work includes temporal clustering and finding periodicities in entity dynamics over large-scale snapshots of the LOD cloud with about 100 million triples per week for more than three years.

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Ansgar Scherp

Manually selecting subsets of photos from large collections in order to present them to friends or colleagues or to print them as photo books can be a tedious task. Today, fully automatic approaches are at hand for supporting users. They make use of pixel information extracted from the images, analyze contextual information such as capture time and focal aperture, or use both to determine a proper subset of photos. However, these approaches miss the most important factor in the photo selection process: the user. The goal of our approach is to consider individual interests. By recording and analyzing gaze information from the user's viewing photo collections, we obtain information on user's interests and use this information in the creation of personal photo selections. In a controlled experiment with 33 participants, we show that the selections can be significantly improved over a baseline approach by up to 22% when taking individual viewing behavior into account. We also obtained significantly better results for photos taken at an event participants were involved in compared with photos from another event.

A Framework for Iterative Signing of Graph Data on the Web

Ansgar Scherp

Existing algorithms for signing graph data typically do not cover the whole signing process. In addition, they lack distinctive features such as signing graph data at different levels of granularity, iterative signing of graph data, and signing multiple graphs. In this paper, we introduce a novel framework for signing arbitrary graph data provided, e g., as RDF(S), Named Graphs, or OWL. We conduct an extensive theoretical and empirical analysis of the runtime and space complexity of different framework configurations. The experiments are performed on synthetic and real-world graph data of different size and different number of blank nodes. We investigate security issues, present a trust model, and discuss practical considerations for using our signing framework. We released a Java-based open source implementation of our software framework for iterative signing of arbitrary graph data provided, e. g., as RDF(S), Named Graphs, or OWL. The software framework is based on a formalization of different graph signing functions and supports different configurations. It is available in source code as well as pre-compiled as .jar-file. The graph signing framework exhibits the following unique features: - Signing graphs on different levels of granularity - Signing multiple graphs at once - Iterative signing of graph data for provenance tracking - Independence of the used language for encoding the graph (i. e., the signature does not break when changing the graph representation) The documentation of the software framework and its source code is available from: http://icp.it-risk.iwvi.uni-koblenz.de/wiki/Software_Framework_for_Signing_Graph_Data

SchemEX -- Building an Index for Linked Open Data

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About Multimedia Presentation Generation and Multimedia Metadata: From Synthesis to Analysis, and Back?

1. Multimedia Presentation Generation and Multimedia Metadata: From Synthesis to Analysis, and Back? Ansgar Scherp

2. ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 2 Multimedia Presentation Generation Ansgar Scherp, Susanne Boll: Paving the Last Mile for Multi-Channel Multimedia Presentation Generation. MMM 2005: 190-197 (Best Paper Award) It is all about the media metadata not the media content itself ... • Dynamic generation • Multi-channel output

3. ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 3 Authoring Tool xSMART • First to enable context-driven multimedia authoring • Using media metadata and user profile information Ansgar Scherp, Susanne Boll: Context-driven smart authoring of multimedia content with xSMART. ACM Multimedia 2005: 802-803

4. ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 4 We Care So Much About Metadata! • Really? So how can it be that we ... … almost loose all metadata after assemle? Semantics Barrier!

5. ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 5 Idea: Emergent Semantics • Semantics that emerges from putting the single pieces of media content together • “Sum is more than just the set of its pieces” • Wait a minute: how to describe the semantics?

6. ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 6 Example: Emergent Semantics S. Boll, P. Sandhaus, A. Scherp, U. Westermann: Semantics, content, and structure of many for the creation of personal photo albums. ACM Multimedia 2007: 641-650 • Rule-based • Customizable • Multi-channel

7. Slide 7ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Embedding Metadata in SMIL <smil xmlns="http://w3.org/2006/SMIL30/..."> <head>  <metadata id="meta-rdf"> </metadata>  </head> <body>  <seq id="mainPresentation"> <par xml:id=“achievements">...</par> <par xml:id=“risks">...</par> </seq> </body> </smil> ???

8. Slide 8ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Solution Approach Formal modeling framework for representing and integrating multimedia metadata Multimedia Metadata Ontology (M3O) Carsten Saathoff, Ansgar Scherp: Unlocking the semantics of multimedia presentations in the web with the multimedia metadata ontology. WWW 2010: 831-840

9. Slide 9ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu ACM Multimedia 2010, Firence, Italy

10. ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 10 Some Cool Semantics Analyses ... Text Cancer (18899-3) Research (10436-6) USA (17829-1) … Smart Photo Selection (CHI'14) Gaze Analysis in Search (IUI'14) Document Classification (KCAP'15) (Nomination for Best Paper Award) Text Extraction from Images (DocEng'15)

11. ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 11 MOVING back to the roots?! How to deal with the vast information in research and innovation? Generating cMOOCs Personalized advise (log analyses) Advanced search and retrieval EU H2020 project since April 2016, budget: 3.5 M €, URL: http://moving-project.eu/

12. ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 12 Use Cases • Research on business information by public administrators – Understand complex situations – Increase compliance with current laws and regulations • Managing & mining research information – Reduce information overload – Assist in evaluating and selecting relevant information resources

13. ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 13 Example: EconBiz's Guided Walk • URL: http://www.econbiz.de/eb/gw/

14. ACM Multimedia 2016 Ansgar Scherp – a.scherp@zbw.eu Slide 14 Thank you ... ... for giving me a good reason and the fun to burrow in my own paper archive! Contact me! Email: a.scherp@zbw.eu • Twitter: https://twitter.com/ansgarscherp • Slideshare: http://de.slideshare.net/ascherp • KD-Website: http://www.zbw.eu/en/research/knowledge-discovery/ http://www.kd.informatik.uni-kiel.de/en/

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