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Dear Sir/Madam:
Please find below an anouncement of a special
section on "Distributed and Mobile Data Mining" of IEEE Transactions on Systems,
Man, and Cybernetics Part B for circulation on your mailing list.
Thanks.
Sanghamitra
=========================================================
Dr. Sanghamitra Bandyopadhyay Email: [EMAIL PROTECTED] Machine Intelligence Unit URL: http://www.isical.ac.in/~sanghami Indian Statistical Institute Fax: 91 33 2578 3357 203 B. T. Road, Kolkata 700 108. Tel: 91 33 2577 8085 extn 3114 INDIA ========================================================= ANNOUNCEMENT
--------------------------- IEEE Transactions on Systems, Man, and Cybernetics
Part B
Special Section on Distributed and Mobile Data Mining Volume 34, Number 6, December 2004 ISSN 1083-4419 GUEST EDITORS: CONTENTS Guest Editorial - H. Kargupta, S. Bandyopadhyay and B. H. Park
NOTE: Advances in computing and communication over wired and
wireless
networks have resulted in many pervasive distributed computing environments. These environments often come with different distributed sources of data and computation. Mining in such environments naturally calls for proper utilization of these distributed resources. Moreover, in many privacy sensitive applications different, possibly multiparty, data sets collected at different sites must be processed in a distributed fashion without collecting everything to a single central site. However, most off-the-shelf data mining systems are designed to work as a monolithic centralized application which does not work well in many of the emerging distributed, ubiquitous, possibly privacy-sensitive data mining applications. Distributed data mining (DDM) offers an alternate approach to address this problem of mining data using distributed resources. This special section offers an interesting blend of papers that deals with several emerging DDM concepts and techniques. SPECIAL SECTION PAPERS PAPER #1
---------------- "Association Rule Mining in Peer-to-Peer Systems" Ran Wolff, University of Maryland, Baltimore County
Assaf Schuster, Technion, Israel Institute of Technology, Israel NOTE: This paper offers an algorithm for mining distributed data
in
peer-to-peer (P2P) environments such as file sharing networks and sensor networks. This is a relatively new DDM topic and it seems to have many exciting applications. This particular paper presents a distributed technique for learning association rules in a P2P environment. PAPER #2 ---------------- "Parallel and Distributed Methods for Incremental Frequent Itemset Mining" M. E. Otey, Ohio State University, Columbus, USA
S. Parthasarathy, Ohio State University, Columbus, USA C. Wang, Ohio State University, Columbus, USA A. Veloso, Universidade Federal de Minas Gerais, Brazil W. Meira, Jr, Universidade Federal de Minas Gerais, Brazil NOTE: This paper presents a parallel version of an incremental
frequent
itemset mining. It also offers an asynchronous technique for distributed incremental frequent itemset mining. Such techniques are likely to be very useful for monitoring distributed data streams. Network intrusion detection is one such application. PAPER #3 ---------------- "Distributed Data Mining on Grids: Services, Tools and Applications" Mario Cannataro, Universita di Catanzaro, Italy
Antonio Congiusta, Universita della Calabria, Italy Andrea Pugliese, Universita della Calabria, Italy Domenico Talia, Universita della Calabria, Italy Paolo Trunfio, Universita della Calabria, Italy NOTE: Grid computing is playing an increasingly important role in
the
world of parallel/distributed computing. Large-scale resource sharing capability of the grid is making it appealing for many data intensive applications. This paper describes a framework for distributed data mining on Grids. It offers a perspective of the various systems-issues in building a Grid-based DDM application. PAPER #4 ---------------- "A Hybrid Model for Improving Response Time in Distributed Data Mining" Shonali Krishnaswamy, Seng W. Loke, and Arkady Zaslasvky
Monash University, Melbourne, Australia NOTE: This paper by offers an approach for improving response time and scalability in DDM. It presents a multi-agent approach that computes models of the computing and communication behavior of the application environment for optimally exploiting the resources during the execution of a distributed data mining algorithm. CORRESPONDENCE PAPER #5
---------------- "Mining Multilevel and Location-Aware Service Patterns in Mobile Web Environments" Shin-Mu Tseng and Ching-Fu Tsui
National Cheng Kung University, Taiwan, R.O.C NOTE: This paper deals with the problem of mining location-aware
service
patterns in mobile web environments. This application involves a mobile environment and multilevel association rule mining. Such applications are likely to be useful for meta-level mining of models and the data in mobile web environments. It may also lead to distributed location-sensitive data mining applications running on-board various mobile devices |
