AIS-ADM-07 Workshop

Keynote Speakers

1 Hillol Kargupta
Associate Professor in the Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
Peer-to-Peer Data Mining: Local Algorithms, Privacy Issues, and Games (abstract)

2 Ning Zhong
Head of Knowledge Information Systems Laboratory, and Professor in Department of Life Science and Informatics at Maebashi Institute of Technology, Japan, and Director and Adjunct Professor in the International WIC Institute, Beijing University of Technology, China
WI Based Multi-Aspect Data Analysis in a Brain Informatics Portal (abstract)

3 Sandip Sen
Professor of Computer Science in the University of Tulsa
Robust Agent Communities (abstract)

4 Vladimir F. Khoroshevsky
Head of Applied Intelligent Systems Division in Computer Centre Russian Academy of Sciences, Moscow; Ontos AG Chief Scientist
Ontos Solutions for Semantic Web: Text Mining, Navigation and Analytics (abstract)

Hillol Kargupta
Associate Professor in the Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
Peer-to-Peer Data Mining: Local Algorithms, Privacy Issues, and Games
Abstract. Peer-to-Peer (P2P) networks are gaining increasing popularity in many distributed applications such as file-sharing, network storage, web caching, searching and indexing of relevant documents and P2P network-threat analysis. Many of these applications require scalable analysis of data over a P2P network. This talk will focus on P2P distributed data stream mining and monitoring. It will first discuss the foundation of local P2P algorithms for data analysis and present some examples. Next it will discuss some of the privacy concerns with P2P data mining and point out the problems of existing privacy-preserving multi-party data mining techniques which usually assume that the parties abide by the protocols as expected, compute whatever is needed, communicate correctly following the rules, and do not collude with other parties for exposing third party sensitive data. Rest of the talk will argue that most of these nice assumptions fall apart in real-life applications of privacy-preserving distributed data mining (PPDM). The talk will offer a more realistic formulation of the PPDM problem as a multi-party game where each party tries to maximize its own objective or utility. The talk will develop a game-theoretic framework and discuss some recent results. It will also discuss equilibrium-analysis of such games and describe some local distributed algorithms that are based on such game theoretic frameworks.

Hillol Kargupta is an Associate Professor in the Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County. He received the PhD degree in computer science from the University of Illinois at Urbana-Champaign in 1996. He is also a co-founder of Agnik LLC, a data analytics company for distributed, mobile, and embedded environments. His research interests include mobile and distributed data mining and computation in biological process of gene expression. Dr. Kargupta won a US National Science Foundation CAREER award in 2001 for his research on ubiquitous and distributed data mining. He along with his coauthors received the best paper award at the 2003 IEEE International Conference on Data Mining for a paper on privacy-preserving data mining. He won the 2000 TRW Foundation Award and the 1997 Los Alamos Award for Outstanding Technical Achievement. His research has been funded by the US National Science Foundation, US Air Force, Department of Homeland Security, NASA, and various other organizations.

He has published more than 80 peer-reviewed articles in journals, conferences, and books. He has co-edited two books: Advances in Distributed and Parallel Knowledge Discovery, AAAI/MIT Press, and Data Mining: Next Generation Challenges and Future Directions, AAAI/MIT Press. He is an associate editor of the IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics, Part B and Statistical Analysis and Data Mining Journal. He was the program-co-chair of the 2005 SIAM Data Mining Conference, Program vice-chair of 2005 PKDD Conference, Program vice-chair of 2005 IEEE International Data Mining Conference, Program Vice Chair for 2005 Euro-PAR Conference, Associate General Chair of the 2003 ACM SIGKDD Conference, and chair of the 2002 NSF Next Generation Data Mining Workshop among others. He regularly serves in the organizing and program committee of many data mining conferences. More information about him can be found at http://www.cs.umbc.edu/~hillol.

Ning Zhong
Head of Knowledge Information Systems Laboratory, and Professor in Department of Life Science and Informatics at Maebashi Institute of Technology, Japan, and Director and Adjunct Professor in the International WIC Institute, Beijing University of Technology, China
WI Based Multi-Aspect Data Analysis in a Brain Informatics Portal
Abstract. In order to investigate human information processing mechanism systematically, Web intelligence (WI) based portal techniques are required for brain data measurement, management and analysis. Building a brain informatics portal is, in fact, to develop a data mining grid centric multi-layer grid system, on which various data mining agents are deployed, for multi-aspect data analysis. We propose an approach for collecting, modeling, transforming, managing, and mining multiple human brain data obtained from systematic fMRI/EEG experiments. The proposed approach provides a new way in Brain Informatics for automatic analysis and understanding of human brain data to replace human-expert centric visualization. We attempt to change the perspective of cognitive scientists from a single type of experimental data analysis towards a holistic view at a long-term, global field of vision to understand the principle, models and mechanisms of human information processing.

Ning Zhong received the Ph.D. degree in the Interdisciplinary Course on Advanced Science and Technology from the University of Tokyo. He is currently head of Knowledge Information Systems Laboratory, and a professor in Department of Systems and Information Engineering at Maebashi Institute of Technology, Japan. He is also director and an adjunct professor in the International WIC Institute (WICI), Beijing University of Technology. He has conducted research in the areas of knowledge discovery and data mining, rough sets and granular-soft computing, Web intelligence, intelligent agents, brain informatics, and knowledge information systems, with over 150 journal and conference publications and 10 books. He is the editor-in-chief of the Web Intelligence and Agent Systems journal (IOS Press), and Annual Review of Intelligent Informatics (World Scientific), associate editor of IEEE Transactions on Knowledge and Data Engineering, and the Knowledge and Information Systems journal (Springer), a member of the editorial board of Transactions on Rough Sets (Springer), and the editorial board of Advanced Information and Knowledge Processing (AI&KP) book series (Springer), Frontiers in AI and Applications book series (IOS Press), and editor (the area of intelligent systems) of the Encyclopedia of Computer Science and Engineering (Wiley). He is the co-chair of Web Intelligence Consortium (WIC), chair of the IEEE Computer Society Technical Committee on Intelligent Informatics (TCII), member of the steering committee of IEEE International Conferences on Data Mining (ICDM), IEEE GrC Task Force Committee and the Advisory Board, the steering committee of International Rough Set Society.

He has served or is currently serving on the program committees of over 80 international conferences and workshops, including IEEE ICDM'02 (conference chair), IEEE ICDM'06 (program chair), IEEE/WIC WI-IAT'03 (conference chair), IEEE/WIC/ACM WI-IAT'04 (program chair), and IJCAI'03 (advisory committee member). He is a senior member of IEEE, and a member of IPSJ, JSAI, IEEE-CS, IEEE-SMC, ACM, AAAI, and IRSS.

Sandip Sen
Professor of Computer Science in the University of Tulsa
Robust Agent Communities
Abstract. We believe that intelligent information agents will represent their users interest in electronic marketplaces and other forums to trade, exchange, share, identify, and locate goods and services. Such information worlds will present unforeseen opportunities as well as challenges that can be best addressed by robust, self-sustaining agent communities. An agent community is a stable, adaptive group of self-interested agents that share common resources and must coordinate their efforts to effectively develop, utilize and nurture group resources and organization. More specifically, agents will need mechanisms to benefit from complementary expertise in the group, pool together resources to meet new demands and exploit transient opportunities, negotiate fair settlements, develop norms to facilitate coordination, exchange help and transfer knowledge between peers, secure the community against intruders, and learn to collaborate effectively. In this talk, I will summarize some of our research results on trust-based computing, negotiation, and learning that will enable intelligent agents to develop and sustain robust, adaptive, and successful agent communities.

Sandip Sen is a Professor of Computer Science in the University of Tulsa with primary research interests in multiagent systems, machine learning, and evolutionary computation. He completed his PhD in the area of intelligent, distributed scheduling from the University of Michigan in December, 1993. He has authored approximately 2000 papers in workshops, conferences, and journals in several areas of artificial intelligence. In 1997 he received the prestigious CAREER award given to outstanding young faculty by the National Science Foundation. He has served on the program committees of most major national and international conferences in the field of intelligent agents. He has chaired multiple international workshops and symposia on agent learning and reasoning. He has also presented several tutorials on multiagent systems in association with the leading international conferences on autonomous agents and multiagent systems.

Vladimir F. Khoroshevsky
Head of Applied Intelligent Systems Division in Computer Centre Russian Academy of Sciences, Moscow; Ontos AG Chief Scientist
Ontos Solutions for Semantic Web: Text Mining, Navigation and Analytics
Abstract. The talk will discuss the problem of development and implementation of semantic navigation through Web-content. Multi-agent architecture of a solution for Semantic Web and innovative services are presented. In the context of the proposed solution Web mining is carried out by special OntosMiner agents, which provide the ontology-driven processing of multilingual text collections on the basis of the special kind of content extraction technologies. First evaluation results of the presented solution are discussed as well.

DrSc., Prof. Vladimir Khoroshevsky has worked extensively in the fields of software engineering, artificial intelligence and natural language processing. He has more than 120 publications in domains, in particularly, in Knowledge-Based Software Engineering, Knowledge Representation, Multi-Agent Systems, Natural Language Processing, Information and Content Extraction, Ontology Engineering, etc., author of 3 monographs published by known Russian Publishers "Radio and Svyaz" and "Piter Press". He is also Corresponding Member of Russian Academy of Natural Sciences, Member of the Board of Russian Association of Artificial Intelligence (RAAI), Board Member of RAAI Scientific Journal "AI News", member of Program Committees of different International Conferences, in particularly, CAI '88-'06, AIMSA '98-'02, JCKBSE '96-'06, RANLP '2003, etc.

Main Research Interests: Artificial Intelligence Software, Knowledge Based Software Engineering; Expert Systems and Tools; Multi Agent Systems; Applied Semiotics, Semantic Web; Natural Language Processing, Information and Content Extraction; Knowledge Management, Ontology Engineering.