Call for Paper Conference

*INTERNET MULTIMEDIA MINING*

http://research.microsoft.com/IMM2009/

In conjunction with IEEE International Conference on Data Mining 2009

December 6, Miami, Florida, USA

Aims and scopeWith the explosion of video and image data available on the Internet, online multimedia applications become more and more important. Moreover, mining semantics and other useful information from large-scale Internet multimedia data to facilitate online and local multimedia content analysis, search and other related applications has also gained more and more attention from both academia and industry. On the one hand, the rapid increase of online multimedia data brings new challenges to multimedia content analysis, multimedia retrieval and related multimedia applications, especially in scalability. Both computation cost and performance of many existing techniques are far from satisfactory. On the other hand, Internet also provides us with new opportunities to attack these challenges as well has conventional problems encountered in multimedia mining, content analysis, image/video understanding and computer vision. That is, the massive associated metadata, context and social information available on the Internet, as well as the massive grassroots Internet users, are invaluable resources that can be leveraged to solve the aforementioned difficulties.

Recently more and more researchers are realizing both the challenges and the opportunities for multimedia research brought by the Internet. This full-day workshop aims at bringing together high-quality and novel research works on "Internet Multimedia Mining".

One of the major obstacles of "Internet Multimedia Mining" research is the difficulty in forming a "good" dataset for algorithm developing, system prototyping and performanceevaluation. Together with this workshop, we release a benchmark dataset, which is based on real Internet multimedia data and real Internet multimedia search engines. Submissions to this workshop are encouraged to use this dataset, but papers/demos working on other Internet-based datasets are also welcome.

MSRA Multimedia Dataset

MSRA-MM Version 1 dataset is ready for shipping. Detailed information about the dataset can

be find at: http://research.microsoft.com/apps/pubs/default.aspx?id=79942. Please contact the

dataset chair (Meng Wang: mengwang@microsoft.com) to request the data.

MSRA-MM Version 2 dataset (10 times larger with more metadata) will be ready around June 15.

Please submit your request to the dataset chair.

Topics of Interest

Topics of interest for this workshop include, but are not limited to:

* Internet video/image/audio annotation, classification, tagging, search ranking and reranking by combining textual description and video/image content.

* General video/image/audio annotation, classification, tagging, search ranking and reranking by exploiting Internet data and/or users. Approaches which can handle large-scale data/users are more preferred.

* Video/image/audio processing and analyses using Internet data as a knowledge base.

* Social media processing, such as online media authoring and sharing, tag recommendation, tag filtering, tag ranking, and search ranking based on image/video/audio social context.

* Knowledge mining from Internet multimedia data, such as mining semantic distance of keywords or images, mining video/image/audio copy relationships (e.g., given a video/image/audio, to find all videos/images on the Internet that have the same content with the video/image, either entirely or partially), mining trends of multimedia consuming/sharing, mining knowledge (for example, "photo encyclopedia"", from massive amount of multimedia content on the Internet, etc.

* Web-scale content-based multimedia retrieval (for example, approaches based on large-scale high-dimensional feature indexing).

* Other online multimedia mining applications, such as multimedia advertising, multimedia recommendation, as well as location/GPS/geography-enabled multimedia, multimedia sensor network over the Internet, etc.

Paper Submission

We accept two forms of submissions: regular full papers and demonstrations. Regular submissions for this workshop are required to use the same format as regular ICDM long papers (a maximum of 10 pages in the IEEE 2-column format). And demonstration submission requires a 1- or 2-page demo description. We especially encourage long-paper authors to submit a demo also. All submissions will be peer-reviewed by at least 3 members of the program committee. Extended version of selected papers will be invited to submit to a special issue of a top journal in data mining or multimedia area.

Award

The workshop will present two awards: a best paper award and a best demonstration award, judged by a separate awards committee.

Important Dates

• * August 8 Submission of full paper
• * August 29 Submission of demo paper
• * September 8 Notification of Acceptance
• * September 28 Camera-Ready Paper Due
• * December 6 Workshop
  

In conjunction with the 2009 IEEE International Conference on Data Mining (ICDM 2009)
Miami, USA, December 6 - 9, 2009

http://sadm09.isti.cnr.it

Knowledge Discovery is generally described as a process for the automatic extraction of interesting, useful, and previously unknown knowledge from data. It is expected that data characteristics, data semantics as well as the knowledge that already exists about the data can be directly incorporated into the knowledge discovery process. However, semantic and reasoning aspects may intervene in several steps of the discovery process, and now the question is how these aspects interact within the process and the forms that this can take. In data pre-processing, semantic aspects may help in (1) identifying source data of interest, (2) enriching the data with additional domain information, and (3) generating more meaningful and human understandable patterns, once the generated patterns are directly related to the input data. During the mining task, semantics may be used as constraints, thus allowing (1) search space reduction, (2) pattern pruning, and (3) the development of more efficient algorithms. Background knowledge may intervene in the post-processing step helping in the explanation of large amounts of patterns, typically difficult to interpret. The objective of SADM is to introduce methods which can explicitly consider data semantics, background knowledge, or reasoning in the mining process. This knowledge has to be represented/formalized in a knowledge repository, such as ontologies, conceptual schemas, knowledge bases, etc. The main aspect is that this knowledge has to be explicitly incorporated into the KDD process, where the algorithms for data preprocessing, data mining or post-processing make use of this knowledge to improve the KDD process. The key idea is to develop a more general understanding about how to exploit data semantics and background knowledge, and to create standardized procedures for designing more intelligent data mining methods. We believe that such an effort can lead to the development of the science of semantic data mining.

* Journal Special Issue*
The selected Best Paper will be recommended for publication in a special issue of Knowledge and Information Systems (KAIS) journal edited by Springer..

* Topics of Interest*:
The workshop will seek submissions where semantic information plays an explicit role in the knowledge discovery process. The intent of the workshop is to bring together researchers and practitioners interested in semantic aspects in data mining from a wide range of possible data mining subareas, including: web mining, medical data mining, spatio-temporal data mining, ubiquitous knowledge discovery, and privacy-preserving data mining.

The aim of the workshop is to receive contributions in the following topics, which are not exclusive:

* Techniques to embed semantics into the discovery process
* Semantics in data pre-processing and post-processing
* Semantic-based pruning
* Knowledge-based data mining algorithms
* Semantic-based techniques for feature selection
* Ontologies and data pre-processing
* Ontology-based evaluation of discovered patterns
* Data mining query languages
* Constraint-based data mining
* Semantics for Knowledge representation, interpretation, and reasoning
* Semantics in spatial and spatio-temporal data mining
* Conceptual modeling and data mining
* Semantic-based KDD processes and frameworks
* Semantics in biological data mining
* Semantics for uncertainty handling in data mining
* Semantics in privacy-preserving data mining
* Semantics in social network data mining

* Important Dates*:
* July 17, 2009 Due date for full workshop papers
* September 8, 2009 : Notification of paper acceptance to authors
* September 28, 2009 : Camera-ready of accepted papers
* December 6, 2009 : Workshop date

*Preliminary Program Committee List *(***to be completed***):
* Luis Otavio Alvares, Universidade Federal do Rio Grande do Sul, Brazil
* Ronnie Alves, Institute of Developmental Biology and Cancer, Faculte des Sciences, France
* Miriam Baglioni, Department of Computer Science, University of Pisa, Italy
* Jean-Francois Boulicaut, INSA, University of Lyon, France
* Toon Calders, Eindhoven Technical University, The Netherlands
* Ivan Janciak, University of Wien, Austria
* Jose Antonio Fernandes de Macedo, EPFL, Switzerland
* Donato Malerba, University of Bari, Italy
* Giuseppe Manco, ICAR CNR, Cosenza, Italy
* Vipin Kumar, University of Minnesota, USA
* Stanley Oliveira, Embrapa Informatica - São Paulo, Brazil
* Solange Rezende, Universidade Federal de São Carlos, Brazil
* Giuseppe Psaila, University of Bergamo, Italy
* Yannis Theodoridis, University of Pireus, Greece
* Monica Wachowicz, Wageningen University, The Netherland
* Mohammed Zaki, Rensselaer Polytechnic Institute, USA

Submission Guidelines and Review Process:
High quality research papers in the relevant areas are solicited. Original papers exploring new directions will receive especially careful consideration. Papers that have already been accepted or are currently under review for other conferences or journals will not be considered. All accepted workshop papers will be included in the IEEE ICDM Workshop Proceedings,
to be published by the IEEE Computer Society Press. Therefore, at least one author of the accepted paper must register to attend the workshop. Paper submissions should be limited to a maximum of 8 pages in PDF format, in the IEEE 2-column style (see the IEEE Computer Society Press Proceedings Author Guidelines at http://www.computer.org/portal/pages/cscps/cps/final/icdm06.xml).
All papers will be reviewed by the Program Committee on the basis of technical quality, relevance to workshop topics, originality, significance, and clarity. We will follow the double blind review process. Authors must hence not use identifying information in the text of the paper and bibliographies must be referenced to preserve anonymity. Please use the Submission Form on the ICDM'09 website to submit your paper.

In conjunction with the 2009 IEEE International Conference on Data Mining (ICDM 2009)
Miami, USA, December 6 - 9, 2009

http://sadm09.isti.cnr.it

Knowledge Discovery is generally described as a process for the automatic extraction of interesting, useful, and previously unknown knowledge from data. It is expected that data characteristics, data semantics as well as the knowledge that already exists about the data can be directly incorporated into the knowledge discovery process. However, semantic and reasoning aspects may intervene in several steps of the discovery process, and now the question is how these aspects interact within the process and the forms that this can take. In data pre-processing, semantic aspects may help in (1) identifying source data of interest, (2) enriching the data with additional domain information, and (3) generating more meaningful and human understandable patterns, once the generated patterns are directly related to the input data. During the mining task, semantics may be used as constraints, thus allowing (1) search space reduction, (2) pattern pruning, and (3) the development of more efficient algorithms. Background knowledge may intervene in the post-processing step helping in the explanation of large amounts of patterns, typically difficult to interpret. The objective of SADM is to introduce methods which can explicitly consider data semantics, background knowledge, or reasoning in the mining process. This knowledge has to be represented/formalized in a knowledge repository, such as ontologies, conceptual schemas, knowledge bases, etc. The main aspect is that this knowledge has to be explicitly incorporated into the KDD process, where the algorithms for data preprocessing, data mining or post-processing make use of this knowledge to improve the KDD process. The key idea is to develop a more general understanding about how to exploit data semantics and background knowledge, and to create standardized procedures for designing more intelligent data mining methods. We believe that such an effort can lead to the development of the science of semantic data mining.

* Journal Special Issue*
The selected Best Paper will be recommended for publication in a special issue of Knowledge and Information Systems (KAIS) journal edited by Springer..

* Topics of Interest*:
The workshop will seek submissions where semantic information plays an explicit role in the knowledge discovery process. The intent of the workshop is to bring together researchers and practitioners interested in semantic aspects in data mining from a wide range of possible data mining subareas, including: web mining, medical data mining, spatio-temporal data mining, ubiquitous knowledge discovery, and privacy-preserving data mining.

The aim of the workshop is to receive contributions in the following topics, which are not exclusive:

* Techniques to embed semantics into the discovery process
* Semantics in data pre-processing and post-processing
* Semantic-based pruning
* Knowledge-based data mining algorithms
* Semantic-based techniques for feature selection
* Ontologies and data pre-processing
* Ontology-based evaluation of discovered patterns
* Data mining query languages
* Constraint-based data mining
* Semantics for Knowledge representation, interpretation, and reasoning
* Semantics in spatial and spatio-temporal data mining
* Conceptual modeling and data mining
* Semantic-based KDD processes and frameworks
* Semantics in biological data mining
* Semantics for uncertainty handling in data mining
* Semantics in privacy-preserving data mining
* Semantics in social network data mining

* Important Dates*:
* July 17, 2009 Due date for full workshop papers
* September 8, 2009 : Notification of paper acceptance to authors
* September 28, 2009 : Camera-ready of accepted papers
* December 6, 2009 : Workshop date

*Preliminary Program Committee List *(***to be completed***):
* Luis Otavio Alvares, Universidade Federal do Rio Grande do Sul, Brazil
* Ronnie Alves, Institute of Developmental Biology and Cancer, Faculte des Sciences, France
* Miriam Baglioni, Department of Computer Science, University of Pisa, Italy
* Jean-Francois Boulicaut, INSA, University of Lyon, France
* Toon Calders, Eindhoven Technical University, The Netherlands
* Ivan Janciak, University of Wien, Austria
* Jose Antonio Fernandes de Macedo, EPFL, Switzerland
* Donato Malerba, University of Bari, Italy
* Giuseppe Manco, ICAR CNR, Cosenza, Italy
* Vipin Kumar, University of Minnesota, USA
* Stanley Oliveira, Embrapa Informatica - São Paulo, Brazil
* Solange Rezende, Universidade Federal de São Carlos, Brazil
* Giuseppe Psaila, University of Bergamo, Italy
* Yannis Theodoridis, University of Pireus, Greece
* Monica Wachowicz, Wageningen University, The Netherland
* Mohammed Zaki, Rensselaer Polytechnic Institute, USA

Submission Guidelines and Review Process:
High quality research papers in the relevant areas are solicited. Original papers exploring new directions will receive especially careful consideration. Papers that have already been accepted or are currently under review for other conferences or journals will not be considered. All accepted workshop papers will be included in the IEEE ICDM Workshop Proceedings,
to be published by the IEEE Computer Society Press. Therefore, at least one author of the accepted paper must register to attend the workshop. Paper submissions should be limited to a maximum of 8 pages in PDF format, in the IEEE 2-column style (see the IEEE Computer Society Press Proceedings Author Guidelines at http://www.computer.org/portal/pages/cscps/cps/final/icdm06.xml).
All papers will be reviewed by the Program Committee on the basis of technical quality, relevance to workshop topics, originality, significance, and clarity. We will follow the double blind review process. Authors must hence not use identifying information in the text of the paper and bibliographies must be referenced to preserve anonymity. Please use the Submission Form on the ICDM'09 website to submit your paper.

http://sadm09.isti.cnr.it/PC.html


http://www.cs.umbc.edu/ICDM09

Knowledge Discovery is in general described as a process of automatic extraction of new knowledge from data. However, the characteristics of the data, their semantics, the knowledge that already exists about the data, and the context of the application has rarely been considered in the discovery process. Semantic and reasoning aspects may intervene in several steps of knowledge discovery, but how these aspects interact within the process, and the forms that this can take, is an issue that has been given little attention so far.
In data pre-processing, semantic aspects may help in (1) identifying source data of interest, (2) enriching the data with additional domain information, and (3) generating more meaningful and human understandable patterns, once the generated patterns are directly related to the input data. During the mining task, semantics may be used as constraints, thus allowing (1) search space reduction, (2) pattern pruning, and (3) the development of more efficient algorithms. Semantics may intervene in the post-processing step, when helping in the explanation of large amounts of patterns, typically difficult to interpret.
The aim of the workshop is to bring together researchers working on all context, semantic and reasoning aspects in Data Mining, developing techniques to embed semantics in the knowledge discovery process, and taking into account ontological and reasoning issues.

IMPORTANT DATES:
– Submission Deadline: 17 July 2009
– Notication acceptance: 8 September 2009
– Camera-ready: 28 September 2009
– Workshop day 6 December 2009

*BEST PAPER AWARD*

The Workshop organizers will select the best paper of the workshop. The selected Best Paper will be recommended for publication in a special issue of the International Journal of Knowledge and Information Systems (KAIS), edited by Springer.
Topics of Interest:
The workshop will seek submissions where semantic information plays an explicit role in the knowledge discovery process. The intent of the workshop is to bring together researchers and practitioners interested in semantic aspects in data mining from a wide range of possible data mining subareas, including: web mining, medical data mining, spatio-temporal data mining, ubiquitous knowledge discovery, and privacy-preserving data mining.
The aim of the workshop is to receive contributions in the following topics, which are not exclusive:
– Techniques to embed semantics into the discovery process – Semantics in data pre-processing and post-processing – Semantic-based pruning – Knowledge-based data mining algorithms – Semantic-based techniques for feature selection – Ontologies and data pre-processing – Ontology-based evaluation of discovered patterns – Data Mining Query Languages – Constraint-based data mining – Semantics for Knowledge representation, interpretation, and reasoning – Semantics in spatial and spatio-temporal data mining – Conceptual modeling and data mining – Semantic-based KDD processes and frameworks – Techniques using semantics for domain-driven data mining – Semantics in ubiquitous data mining – Semantics in biological data mining – Semantics for uncertainty handling in data mining – Semantics in Privacy-Preserving Data Mining – Semantics in Social Network Data Mining
Organization:
Chairs:
Dr. Vania Bogorny,
Instituto de Informatica,
Universidade Federal do Rio Grande do Sul,
E-mail: vbogorny@inf.ufrgs.br

Dr. Hui Xiong,
Management Science and Information Systems Department,
Rutgers, the State University of New Jersey,
Email: hxiong@rutgers.edu

Dr. Chiara Renso,
Instituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo"
KDD Laboratory, ISTI-CNR Pisa, Italy, CNR di Pisa
Via G. Moruzzi 156124 PISA -Italy
Email: chiara.renso@isti.cnr.it

Program Committee (to be completed):
1. Luis Otavio Alvares, Universidade Federal do Rio Grande do Sul, Brazil
2. Ronnie Alves, Institute of Developmental Biology and Cancer, Faculte des Sciences, France
3. Miriam Baglioni, Department of Computer Science, University of Pisa, Italy
4. Jean-Francois Boulicaut, INSA, University of Lyon, France
5. Toon Calders, Eindhoven Technical University, The Netherlands
6. Ivan Janciak, University of Wien, Austria
7. Jose Antonio Fernandes de Macedo, EPFL, Switzerland
8. Vipin Kumar, University of Minnesota, USA
9. Donato Malerba, University of Bari, Italy
10. Stanley Oliveira, Embrapa Informatica -Sao Paulo, Brazil
11. Giuseppe Psaila, University of Bergamo, Italy
12. Solange Rezende, Universidade Federal de So Carlos, Brazil
13. Yannis Theodoridis, University of Pireus, Greece
14. Monica Wachowicz, UPM, Madrid, Spain
15. Mohammed Zaki, Rensselaer Polytechnic Institute, USA

Submission Guidelines and Review Process:
High quality research papers in the relevant areas are invited. Original papers exploring new directions will receive especially careful consideration. Papers that have already been accepted or are currently under review for other conferences or journals will not be considered.
All accepted workshop papers will be included in the IEEE ICDM Workshop Proceedings, to be published by the IEEE Computer Society Press. Therefore, at least one author of the accepted paper must register to attend the workshop. Paper submissions should be limited to a maximum of 8 pages in PDF format, in the IEEE 2-column style (see the IEEE Computer Society Press Proceedings Author Guidelines at http://www.computer.org/portal/pages/cscps/cps/nal/icdm06.xml).
All papers will be reviewed by the Program Committee on the basis of technical quality, relevance to workshop topics, originality, significance, and clarity. We will follow the double blind review process. Authors must hence not use identifying information in the text of the paper and bibliographies must be referenced to preserve anonymity. Please use the Submission Form on the ICDM'09 website to submit your paper.