Catch the moment: maintaining closed frequent itemsets over a data stream sliding window

This paper considers the problem of mining closed frequent itemsets over a data stream sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed frequent itemsets at any time. Due to time and memory constraints, the synopsis data structure cannot monitor all possible itemsets. However, monitoring only frequent itemsets will make it impossible to detect new itemsets when they become frequent. In this paper, we introduce a compact data structure, the closed enumeration tree (CET), to maintain a dynamically selected set of itemsets over a sliding window. The selected itemsets contain a boundary between closed frequent itemsets and the rest of the itemsets. Concept drifts in a data stream are reflected by boundary movements in the CET. In other words, a status change of any itemset (e.g., from non-frequent to frequent) must occur through the boundary. Because the boundary is relatively stable, the cost of mining closed frequent itemsets over a sliding window is dramatically reduced to that of mining transactions that can possibly cause boundary movements in the CET. Our experiments show that our algorithm performs much better than representative algorithms for the sate-of-the-art approaches. Yun Chi is currently a Ph.D. student at the Department of Computer Science, UCLA. His main areas of research include database systems, data mining, and bioinformatics. For data mining, he is interested in mining labeled trees and graphs, mining data streams, and mining data with uncertainty. Haixun Wang is currently a research staff member at IBM T. J. Watson Research Center. He received the B.S. and the M.S. degree, both in computer science, from Shanghai Jiao Tong University in 1994 and 1996. He received the Ph.D. degree in computer science from the University of California, Los Angeles in 2000. He has published more than 60 research papers in referred international journals and conference proceedings. He is a member of the ACM, the ACM SIGMOD, the ACM SIGKDD, and the IEEE Computer Society. He has served in program committees of international conferences and workshops, and has been a reviewer for some leading academic journals in the database field. Philip S. Yureceived the B.S. Degree in electrical engineering from National Taiwan University, the M.S. and Ph.D. degrees in electrical engineering from Stanford University, and the M.B.A. degree from New York University. He is with the IBM Thomas J. Watson Research Center and currently manager of the Software Tools and Techniques group. His research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing, and performance modeling. Dr. Yu has published more than 430 papers in refereed journals and conferences. He holds or has applied for more than 250 US patents.Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He is associate editors of ACM Transactions on the Internet Technology and ACM Transactions on Knowledge Discovery in Data. He is a member of the IEEE Data Engineering steering committee and is also on the steering committee of IEEE Conference on Data Mining. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001–2004), an editor, advisory board member and also a guest co-editor of the special issue on mining of databases. He had also served as an associate editor of Knowledge and Information Systems. In addition to serving as program committee member on various conferences, he will be serving as the general chairman of 2006 ACM Conference on Information and Knowledge Management and the program chairman of the 2006 joint conferences of the 8th IEEE Conference on E-Commerce Technology (CEC' 06) and the 3rd IEEE Conference on Enterprise Computing, E-Commerce and E-Services (EEE' 06). He was the program chairman or co-chairs of the 11th IEEE International Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, the 2nd IEEE International Workshop on Research Issues on Data Engineering:Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases, and the 2nd IEEE International Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chairman of the 14th IEEE International Conference on Data Engineering and the general co-chairman of the 2nd IEEE International Conference on Data Mining. He has received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 84th plateau of Invention Achievement Awards. He received an Outstanding Contributions Award from IEEE International Conference on Data Mining in 2003 and also an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts" in 1999. Dr. Yu is an IBM Master Inventor. Richard R. Muntz is a Professor and past chairman of the Computer Science Department, School of Engineering and Applied Science, UCLA. His current research interests are sensor rich environments, multimedia storage servers and database systems, distributed and parallel database systems, spatial and scientific database systems, data mining, and computer performance evaluation. He is the author of over one hundred and fifty research papers.Dr. Muntz received the BEE from Pratt Institute in 1963, the MEE from New York University in 1966, and the Ph.D. in Electrical Engineering from Princeton University in 1969. He is a member of the Board of Directors for SIGMETRICS and past chairman of IFIP WG7.3 on performance evaluation. He was a member of the Corporate Technology Advisory Board at NCR/Teradata, a member of the Science Advisory Board of NASA's Center of Excellence in Space Data Information Systems, and a member of the Goddard Space Flight Center Visiting Committee on Information Technology. He recently chaired a National Research Council study on “The Intersection of Geospatial Information and IT” which was published in 2003. He was an associate editor for the Journal of the ACM from 1975 to 1980 and the Editor-in-Chief of ACM Computing Surveys from 1992 to 1995. He is a Fellow of the ACM and a Fellow of the IEEE.     

Query indexing with containment-encoded intervals for efficient stream processing

Many continual range queries can be issued against data streams. To efficiently evaluate continual queries against a stream, a main memory-based query index with a small storage cost and a fast search time is needed, especially if the stream is rapid. In this paper, we study a CEI-based query index that meets both criteria for efficient processing of continual interval queries. This new query index is an indirect indexing approach. It centres around a set of predefined virtual containment-encoded intervals, or CEIs. The CEIs are used to first decompose query intervals and then perform efficient search operations. The CEIs are defined and labeled such that containment relationships among them are encoded in their IDs. The containment encoding makes decomposition and search operations efficient; from the encoding of the smallest CEI containing a data point, the encodings of other containing CEIs can be easily derived. Closed-form formulae for the bounds of the average index storage cost are derived. Simulations are conducted to evaluate the effectiveness of the CEI-based query index and to compare it with alternative approaches. The results show that the CEI-based query index significantly outperforms existing approaches in terms of both storage cost and search time. Kun-Lung Wu received the B.S. degree in electrical engineering from the National Taiwan University, Taipei, Taiwan, the M.S. and Ph.D. degrees in computer science from the University of Illinois at Urbana–Champaign. He is with the IBM Thomas J. Watson Research Center, currently a member of the Software Tools and Techniques Group. His current research interests include data streams, continual queries, mobile computing, Internet technologies and applications, database systems and distributed and parallel computing. He has published extensively and holds various patents in these areas. Dr. Wu is a Senior Member of the IEEE Computer Society and a member of the ACM. He was an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering, 2000–2004. He was the general chair for the 3rd International Workshop on e-Commerce and Web-Based Information Systems (WECWIS 2001). He has served as an organising and program committee member on various conferences. He has received various IBM awards, including IBM Corporate Environmental Affair Excellence Award, Research Division Award and Invention Achievement Awards. He received a best paper award from IEEE EEE 2004. He is an IBM Master Inventor. Shyh-Kwei Chen received the B.S. degree in computer science and information engineering from National Taiwan University, Taipei, Taiwan, in 1983, the M.S. degree in computer science from the University of Minnesota, Minneapolis, in 1987, and the Ph.D. degree in computer science from University of Illinois at Urbana–Champaign, in 1994. Dr. Chen has been with the IBM Thomas J. Watson Research Center, Yorktown Heights, New York since October 1994, where he is currently a research staff member. His current research interests include XML, electronic commerce, business performance management, data engineering and compilers. He is a member of the ACM, the IEEE and the IEEE Computer Society. Philip S. Yu received the B.S. degree in electrical engineering from National Taiwan University, the M.S. and Ph.D. degrees in electrical engineering from Stanford University, and the M.B.A. degree from New York University. He is with the IBM Thomas J. Watson Research Center and is currently manager of the Software Tools and Techniques group. His research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing and performance modelling. Dr. Yu has published more than 400 papers in refereed journals and conferences. He holds or has applied for more than 250 US patents. Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He is an associate editor of ACM Transactions on Internet Technology. He is a member of the IEEE Data Engineering steering committee and is also on the steering committee of IEEE Conference on Data Mining. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001–2004), an editor and advisory board member of IEEE Transactions on Knowledge and Data Engineering and also a guest coeditor of the special issue on mining of databases. He had also served as an associate editor of Knowledge and Information Systems. In addition to serving as program committee member on various conferences, he was the program cochair of the 11th International Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, and the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, and the program chair of the 2nd International Workshop on Research Issues on Data Engineering: Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases and the 2nd International Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chair of the 14th International Conference on Data Engineering and the general cochair of the 2nd IEEE International Conference on Data Mining. He has received several IBM honours, including two IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, two Research Division Awards and the 81st Plateau of Invention Achievement Awards. He received an Outstanding Contributions Award from IEEE International Conference on Data Mining in 2003 and also an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts” in 1999. Dr. Yu is an IBM Master Inventor and was recognised as one of the IBM's 10 top leading inventors in 1999.     

CPU load shedding for binary stream joins

We present an adaptive load shedding approach for windowed stream joins. In contrast to the conventional approach of dropping tuples from the input streams, we explore the concept ofselective processing for load shedding. We allow stream tuples to be stored in the windows and shed excessive CPU load by performing the join operations, not on the entire set of tuples within the windows, but on a dynamically changing subset of tuples that are learned to be highly beneficial. We support such dynamic selective processing through three forms of runtimeadaptations: adaptation to input stream rates, adaptation to time correlation between the streams and adaptation to join directions. Our load shedding approach enables us to integrateutility-based load shedding withtime correlation-based load shedding. Indexes are used to further speed up the execution of stream joins. Experiments are conducted to evaluate our adaptive load shedding in terms of output rate and utility. The results show that our selective processing approach to load shedding is very effective and significantly outperforms the approach that drops tuples from the input streams. Bugra Gedik received the B.S. degree in C.S. from the Bilkent University, Ankara, Turkey, and the Ph.D. degree in C.S. from the College of Computing at the Georgia Institute of Technology, Atlanta, GA, USA. He is with the IBM Thomas J. Watson Research Center, currently a member of the Software Tools and Techniques Group. Dr. Gedik's research interests lie in data intensive distributed computing systems, spanning data-centric peer-to-peer overlay networks, mobile and sensor-based distributed data management systems, and distributed data stream processing systems. His research focus is on developing system-level architectures and techniques to address scalability problems in distributed continual query systems and applications. He is the recipient of the ICDCS 2003 best paper award. He has served in the program committees of several international conferences, such as ICDE, MDM, and CollaborateCom. Kun-Lung Wu received the B.S. degree in E.E. from the National Taiwan University, Taipei, Taiwan, the M.S. and Ph.D. degrees in C.S. both from the University of Illinois at Urbana-Champaign. He is with the IBM Thomas J. Watson Research Center, currently a member of the Software Tools and Techniques Group. His recent research interests include data streams, continual queries, mobile computing, Internet technologies and applications, database systems and distributed computing. He has published extensively and holds many patents in these areas. Dr. Wu is a Senior Member of the IEEE Computer Society and a member of the ACM. He is the Program Co-Chair for the IEEE Joint Conference on e-Commerce Technology (CEC 2007) and Enterprise Computing, e-Commerce and e-Services (EEE 2007). He was an Associate Editor for the IEEE Trans. on Knowledge and Data Engineering, 2000–2004. He was the general chair for the 3rd International Workshop on E-Commerce and Web-Based Information Systems (WECWIS 2001). He has served as an organizing and program committee member on various conferences. He has received various IBM awards, including IBM Corporate Environmental Affair Excellence Award, Research Division Award, and several Invention Achievement Awards. He received a best paper award from IEEE EEE 2004. He is an IBM Master Inventor. Philip S. Yu received the B.S. Degree in E.E. from National Taiwan University, the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University. He is with the IBM Thomas J. Watson Research Center and currently manager of the Software Tools and Techniques group. His research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing, and performance modeling. Dr. Yu has published more than 430 papers in refereed journals and conferences. He holds or has applied for more than 250 US patents. Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He is associate editors of ACM Transactions on the Internet Technology and ACM Transactions on Knowledge Discovery in Data. He is a member of the IEEE Data Engineering steering committee and is also on the steering committee of IEEE Conference on Data Mining. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001–2004), an editor, advisory board member and also a guest co-editor of the special issue on mining of databases. He had also served as an associate editor of Knowledge and Information Systems. In addition to serving as program committee member on various conferences, he will be serving as the general chair of 2006 ACM Conference on Information and Knowledge Management and the program chair of the 2006 joint conferences of the 8th IEEE Conference on E-Commerce Technology (CEC' 06) and the 3rd IEEE Conference on Enterprise Computing, E-Commerce and E-Services (EEE' 06). He was the program chair or co-chairs of the 11th IEEE Intl. Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, the 2nd IEEE Intl. Workshop on Research Issues on Data Engineering: Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases, and the 2nd IEEE Intl. Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chair of the 14th IEEE Intl. Conference on Data Engineering and the general co-chair of the 2nd IEEE Intl. Conference on Data Mining. He has received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 84th plateau of Invention Achievement Awards. He received an Outstanding Contributions Award from IEEE Intl. Conference on Data Mining in 2003 and also an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts” in 1999. Dr. Yu is an IBM Master Inventor. Ling Liu is an associate professor at the College of Computing at Georgia Tech. There, she directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining research issues and technical challenges in building large scale distributed computing systems that can grow without limits. Dr. Liu and the DiSL research group have been working on various aspects of distributed data intensive systems, ranging from decentralized overlay networks, exemplified by peer to peer computing, data grid computing, to mobile computing systems and location based services, sensor network computing, and enterprise computing systems. She has published over 150 international journal and conference articles. Her research group has produced a number of software systems that are either open sources or directly accessible online, among which the most popular ones are WebCQ and XWRAPElite. Dr. Liu is currently on the editorial board of several international journals, including IEEE Transactions on Knowledge and Data Engineering, International Journal of Very large Database systems (VLDBJ), International Journal of Web Services Research, and has chaired a number of conferences as a PC chair, a vice PC chair, or a general chair, including IEEE International Conference on Data Engineering (ICDE 2004, ICDE 2006, ICDE 2007), IEEE International Conference on Distributed Computing (ICDCS 2006), IEEE International Conference on Web Services (ICWS 2004). She is a recipient of IBM Faculty Award (2003, 2006). Dr. Liu's current research is partly sponsored by grants from NSF CISE CSR, ITR, CyberTrust, a grant from AFOSR, an IBM SUR grant, and an IBM faculty award.     

Mining frequent patterns with the pattern tree

Mining frequent patterns with a frequent pattern tree (FP-tree in short) avoids costly candidate generation and repeatedly occurrence frequency checking against the support threshold. It therefore achieves much better performance and efficiency than Apriori-like algorithms. However, the database still needs to be scanned twice to get the FP-tree. This can be very time-consuming when new data is added to an existing database because two scans may be needed for not only the new data but also the existing data. In this research we propose a new data structure, the pattern tree (P-tree in short), and a new technique, which can get the P-tree through only one scan of the database and can obtain the corresponding FP-tree with a specified support threshold. Updating a P-tree with new data needs one scan of the new data only, and the existing data does not need to be re-scanned. Our experiments show that the P-tree method outperforms the FP-tree method by a factor up to an order of magnitude in large datasets. A preliminary version of this paper has been published in theProceedings of the 2002 IEEE International Conference on Data Mining (ICDM ’02), 629–632. Hao Huang: He is pursuing his Ph.D. degree in the Department of Computer Science at the University of Virginia. His research interests are Gird Computing, Data Mining and their applications in Bioinformatics. He received his M.S. in Computer Science from Colorado School of Mines in 2001. Xindong Wu, Ph.D.: He is Professor and Chair of the Department of Computer Science at the University of Vermont, USA. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM, and WWW. Dr. Wu is the Executive Editor (January 1, 1999-December 31, 2004) and an Honorary Editor-in-Chief (starting January 1, 2005) of Knowledge and Information Systems (a peer-reviewed archival journal published by Springer), the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP), and the Chair of the IEEE Computer Society Technical Committee on Computational Intelligence (TCCI). He served as an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering (TKDE) between January 1, 2000 and December 31, 2003, and is the Editor-in-Chief of TKDE since January 1, 2005. He is the winner of the 2004 ACM SIGKDD Service Award. Richard Relue, Ph.D.: He received his Ph.D. in Computer Science from the Colorado School of Mines in 2003. His research interests include association rules in data mining, neural networks for automated classification, and artificial intelligence for robot navigation. He has been an Information Technology consultant since 1992, working with Ball Aerospace and Technology, Rational Software, Natural Fuels Corporation, and Western Interstate Commission for Higher Education (WICHE).     

Summarization – compressing data into an informative representation

In this paper, we formulate the problem of summarization of a data set of transactions with categorical attributes as an optimization problem involving two objective functions – compaction gain and information loss. We propose metrics to characterize the output of any summarization algorithm. We investigate two approaches to address this problem. The first approach is an adaptation of clustering and the second approach makes use of frequent itemsets from the association analysis domain. We illustrate one application of summarization in the field of network data where we show how our technique can be effectively used to summarize network traffic into a compact but meaningful representation. Specifically, we evaluate our proposed algorithms on the 1998 DARPA Off-Line Intrusion Detection Evaluation data and network data generated by SKAION Corp for the ARDA information assurance program. Vipin Kumar is currently William Norris Professor and Head of the Computer Science and Engineering Department at the University of Minnesota. His research interests include high-performance computing and data mining. He has authored over 200 research articles, and has coedited or coauthored nine books including the widely used text booksIntroduction to Parallel Computing andIntroduction to Data Mining, both published by Addison Wesley. He has served as chair/co-chair for many conferences/workshops in the area of data mining and parallel computing, including the IEEE International Conference on Data Mining (2002) and the 15th International Parallel and Distributed Processing Symposium (2001). He serves as the chair of the steering committee of the SIAM International Conference on Data Mining, and is a member of the steering committee of the IEEE International Conference on Data Mining. Dr. Kumar serves or has served on the editorial boards of several journals includingKnowledge and Information Systems,Journal of Parallel and Distributed Computing andIEEE Transactions of Data and Knowledge Engineering (1993–1997). He is a Fellow of the ACM and IEEE, and a member of SIAM. Varun Chandola received his BTech degree in Computer Science from the Indian Institute of Technology, Madras, India, in 2002. He is currently a PhD student in the Computer Science and Engineering Department at the University of Minnesota. His research interests include data mining, cyber-security and machine learning.     

A study of the model and algorithms for handling location-dependent continuous queries

Advances in wireless and mobile computing environments allow a mobile user to access a wide range of applications. For example, mobile users may want to retrieve data about unfamiliar places or local life styles related to their location. These queries are called location-dependent queries. Furthermore, a mobile user may be interested in getting the query results repeatedly, which is called location-dependent continuous querying. This continuous query emanating from a mobile user may retrieve information from a single-zone (single-ZQ) or from multiple neighbouring zones (multiple-ZQ). We consider the problem of handling location-dependent continuous queries with the main emphasis on reducing communication costs and making sure that the user gets correct current-query result. The key contributions of this paper include: (1) Proposing a hierarchical database framework (tree architecture and supporting continuous query algorithm) for handling location-dependent continuous queries. (2) Analysing the flexibility of this framework for handling queries related to single-ZQ or multiple-ZQ and propose intelligent selective placement of location-dependent databases. (3) Proposing an intelligent selective replication algorithm to facilitate time- and space-efficient processing of location-dependent continuous queries retrieving single-ZQ information. (4) Demonstrating, using simulation, the significance of our intelligent selective placement and selective replication model in terms of communication cost and storage constraints, considering various types of queries. Manish Gupta received his B.E. degree in Electrical Engineering from Govindram Sakseria Institute of Technology & Sciences, India, in 1997 and his M.S. degree in Computer Science from University of Texas at Dallas in 2002. He is currently working toward his Ph.D. degree in the Department of Computer Science at University of Texas at Dallas. His current research focuses on AI-based software synthesis and testing. His other research interests include mobile computing, aspect-oriented programming and model checking. Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China, in 1996, and a Master's Degree in Computer Science from the University of Texas at Dallas 2001. He is currently working toward the Ph.D. degree in the Department of Computer Science at the University of Texas at Dallas. Mr. Tu's research interests include distributed systems, wireless communications, mobile computing, and reliability and performance analysis. His Ph.D. research work focuses on the dependent and secure data replication and placement issues in network-centric systems. Latifur R. Khan has been an Assistant Professor of Computer Science department at University of Texas at Dallas since September 2000. He received his Ph.D. and M.S. degrees in Computer Science from University of Southern California (USC) in August 2000 and December 1996, respectively. He obtained his B.Sc. degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, in November of 1993. Professor Khan is currently supported by grants from the National Science Foundation (NSF), Texas Instruments, Alcatel, USA, and has been awarded the Sun Equipment Grant. Dr. Khan has more than 50 articles, book chapters and conference papers focusing in the areas of database systems, multimedia information management and data mining in bio-informatics and intrusion detection. Professor Khan has also served as a referee for database journals, conferences (e.g. IEEE TKDE, KAIS, ADL, VLDB) and he is currently serving as a program committee member for the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD2005), ACM 14th Conference on Information and Knowledge Management (CIKM 2005), International Conference on Database and Expert Systems Applications DEXA 2005 and International Conference on Cooperative Information Systems (CoopIS 2005), and is program chair of ACM SIGKDD International Workshop on Multimedia Data Mining, 2004. Farokh Bastani received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology, Bombay, and the M.S. and Ph.D. degrees in Computer Science from the University of California, Berkeley. He is currently a Professor of Computer Science at the University of Texas at Dallas. Dr. Bastani's research interests include various aspects of the ultrahigh dependable systems, especially automated software synthesis and testing, embedded real-time process-control and telecommunications systems and high-assurance systems engineering. Dr. Bastani was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (IEEE-TKDE). He is currently an emeritus EIC of IEEE-TKDE and is on the editorial board of the International Journal of Artificial Intelligence Tools, the International Journal of Knowledge and Information Systems and the Springer-Verlag series on Knowledge and Information Management. He was the program cochair of the 1997 IEEE Symposium on Reliable Distributed Systems, 1998 IEEE International Symposium on Software Reliability Engineering, 1999 IEEE Knowledge and Data Engineering Workshop, 1999 International Symposium on Autonomous Decentralised Systems, and the program chair of the 1995 IEEE International Conference on Tools with Artificial Intelligence. He has been on the program and steering committees of several conferences and workshops and on the editorial boards of the IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering and the Oxford University Press High Integrity Systems Journal. I-Ling Yen received her B.S. degree from Tsing-Hua University, Taiwan, and her M.S. and Ph.D. degrees in Computer Science from the University of Houston. She is currently an Associate Professor of Computer Science at University of Texas at Dallas. Dr. Yen's research interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet technologies, E-commerce and self-stabilising systems. She has published over 100 technical papers in these research areas and received many research awards from NSF, DOD, NASA and several industry companies. She has served as Program Committee member for many conferences and Program Chair/Cochair for the IEEE Symposium on Application-Specific Software and System Engineering & Technology, IEEE High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications Conference, and IEEE International Symposium on Autonomous Decentralized Systems. She has also served as a guest editor for a theme issue of IEEE Computer devoted to high-assurance systems.     

Privacy-preserving SVM classification

Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What is required is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the nondisclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from data distributed at multiple parties, without disclosing the data of each party to others. Solutions are sketched out for data that is vertically, horizontally, or even arbitrarily partitioned. We quantify the security and efficiency of the proposed method, and highlight future challenges. Jaideep Vaidya received the Bachelor’s degree in Computer Engineering from the University of Mumbai. He received the Master’s and the Ph.D. degrees in Computer Science from Purdue University. He is an Assistant Professor in the Management Science and Information Systems Department at Rutgers University. His research interests include data mining and analysis, information security, and privacy. He has received best paper awards for papers in ICDE and SIDKDD. He is a Member of the IEEE Computer Society and the ACM. Hwanjo Yu received the Ph.D. degree in Computer Science in 2004 from the University of Illinois at Urbana-Champaign. He is an Assistant Professor in the Department of Computer Science at the University of Iowa. His research interests include data mining, machine learning, database, and information systems. He is an Associate Editor of Neurocomputing and served on the NSF Panel in 2006. He has served on the program committees of 2005 ACM SAC on Data Mining track, 2005 and 2006 IEEE ICDM, 2006 ACM CIKM, and 2006 SIAM Data Mining. Xiaoqian Jiang received the B.S. degree in Computer Science from Shanghai Maritime University, Shanghai, 2003. He received the M.C.S. degree in Computer Science from the University of Iowa, Iowa City, 2005. Currently, he is pursuing a Ph.D. degree from the School of Computer Science, Carnegie Mellon University. His research interests are computer vision, machine learning, data mining, and privacy protection technologies.     

Effective classification of noisy data streams with attribute-oriented dynamic classifier selection

Recently, mining from data streams has become an important and challenging task for many real-world applications such as credit card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a new dynamic classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm dynamically selects a single “best” classifier to classify each test instance at run time. Our scheme uses statistical information from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or have low confidence. A preliminary version of this paper was published in the Proceedings of the 4th IEEE International Conference on Data Mining, pp 305–312, Brighton, UK Xingquan Zhu received his Ph.D. degree in Computer Science from Fudan University, Shanghai, China, in 2001. He spent four months with Microsoft Research Asia, Beijing, China, where he was working on content-based image retrieval with relevance feedback. From 2001 to 2002, he was a Postdoctoral Associate in the Department of Computer Science, Purdue University, West Lafayette, IN. He is currently a Research Assistant Professor in the Department of Computer Science, University of Vermont, Burlington, VT. His research interests include Data mining, machine learning, data quality, multimedia computing, and information retrieval. Since 2000, Dr. Zhu has published extensively, including over 40 refereed papers in various journals and conference proceedings. Xindong Wu is a Professor and the Chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, ICML, KDD, ICDM, and WWW, as well as 11 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He is the 2004 ACM SIGKDD Service Award winner. Ying Yang received her Ph.D. in Computer Science from Monash University, Australia in 2003. Following academic appointments at the University of Vermont, USA, she currently holds a Research Fellow at Monash University, Australia. Dr. Yang is recognized for contributions in the fields of machine learning and data mining. She has published many scientific papers and book chapters on adaptive learning, proactive mining, noise cleansing and discretization. Contact her at yyang@mail.csse.monash.edu.au.     

A multi-threshold online smoothing technique for variable rate multimedia streams

Variable bit rate (VBR) compression for media streams allocates more bits to complex scenes and fewer bits to simple scenes. This results in a higher and more uniform visual and aural quality. The disadvantage of the VBR technique is that it results in bursty network traffic and uneven resource utilization when streaming media. In this study we propose an online media transmission smoothing technique that requires no a priori knowledge of the actual bit rate. It utilizes multi-level buffer thresholds at the client side that trigger feedback information sent to the server. This technique can be applied to both live captured streams and stored streams without requiring any server side pre-processing. We have implemented this scheme in our continuous media server and verified its operation across real world LAN and WAN connections. The results show smoother transmission schedules than any other previously proposed online technique. This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC), and IIS-0082826, DARPA and USAF under agreement nr. F30602-99-1-0524, and unrestricted cash/equipment gifts from NCR, IBM, Intel and SUN. Roger Zimmermann is currently a Research Assistant Professor with the Computer Science Department and a Research Area Director with the Integrated Media Systems Center (IMSC) at the University of Southern California. His research activities focus on streaming media architectures, peer-to-peer systems, immersive environments, and multimodal databases. He has made significant contributions in the areas of interactive and high quality video streaming, collaborative large-scale group communications, and mobile location-based services. Dr. Zimmermann has co-authored a book, a patent and more than seventy conference publications, journal articles and book chapters in the areas of multimedia and databases. He was the co-chair of the ACM NRBC 2004 workshop, the Open Source Software Competition of the ACM Multimedia 2004 conference, the short paper program systems track of ACM Multimedia 2005 and will be the proceedings chair of ACM Multimedia 2006. He is on the editorial board of SIGMOD DiSC, the ACM Computers in Entertainment magazine and the International Journal of Multimedia Tools and Applications. He has served on many conference program committees such as ACM Multimedia, SPIE MMCN and IEEE ICME. Cyrus Shahabi is currently an Associate Professor and the Director of the Information Laboratory (InfoLAB) at the Computer Science Department and also a Research Area Director at the NSF's Integrated Media Systems Center (IMSC) at the University of Southern California. He received his M.S. and Ph.D. degrees in Computer Science from the University of Southern California in May 1993 and August 1996, respectively. His B.S. degree is in Computer Engineering from Sharif University of Technology, Iran. He has two books and more than hundred articles, book chapters, and conference papers in the areas of databases and multimedia. Dr. Shahabi's current research interests include Peer-to-Peer Systems, Streaming Architectures, Geospatial Data Integration and Multidimensional Data Analysis. He is currently an associate editor of the IEEE Transactions on Parallel and Distributed Systems (TPDS) and on the editorial board of ACM Computers in Entertainment magazine. He is also the program committee chair of ICDE NetDB 2005 and ACM GIS 2005. He serves on many conference program committees such as IEEE ICDE 2006, ACM CIKM 2005, SSTD 2005 and ACM SIGMOD 2004. Dr. Shahabi is the recipient of the 2002 National Science Foundation CAREER Award and 2003 Presidential Early Career Awards for Scientists and Engineers (PECASE). In 2001, he also received an award from the Okawa Foundations. Kun Fu is currently a Ph.D candidate in computer science from the University of Southern California. He did research at the Data Communication Technology Research Institute and National Data Communication Engineering Center in China prior to coming to the United States and is currently working on large scale data stream recording architectures at the NSF's Integrated Media System Center (IMSC) and Data Management Research Laboratory (DMRL) at the Computer Science Department at USC. He received an MS in engineering science from the University of Toledo. He is a member of the IEEE. His research interests are in the area of scalable streaming architectures, distributed real-time systems, and multimedia computing and networking. Mehrdad Jahangiri was born in Tehran, Iran. He received the B.S. degree in Civil Engineering from University of Tehran at Tehran, in 1999. He is currently working towards the Ph.D. degree in Computer Science at the University of Southern California. He is currently a research assistant working on multidimensional data analysis at Integrated Media Systems Center (IMSC)—Information Laboratory (InfoLAB) at the Computer Science Department of the University of Southern California.     

Real-time classification of variable length multi-attribute motions

Multi-attribute motion data can be generated in many applications/ devices, such as motion capture devices and animations. It can have dozens of attributes, thousands of rows, and even similar motions can have different durations and different speeds at corresponding parts. There are no row-to-row correspondences between data matrices of two motions. To be classified and recognized, multi-attribute motion data of different lengths are reduced to feature vectors by using the properties of singular value decomposition (SVD) of motion data. The reduced feature vectors of similar motions are close to each other, while reduced feature vectors are different from each other if their motions are different. By applying support vector machines (SVM) to the feature vectors, we efficiently classify and recognize real-world multi-attribute motion data. With our data set of more than 300 motions with different lengths and variations, SVM outperforms classification by related similarity measures, in terms of accuracy and CPU time. The performance of our approach shows its feasibility of real-time applications to real-world data. Chuanjun Li is a Ph.D. candidate in Computer Science at the University of Texas at Dallas. His Ph.D. research works primarily on efficient segmentation and recognition of human motion streams, and development of indexing and clustering techniques for the multi-attribute motion data as well as classification of motion data. Dr. Latifur R. Khan has been an Assistant Professor of Computer Science Department at University of Texas at Dallas since September, 2000. He received his Ph.D. and M.S. degree in Computer Science from University of Southern California (USC) in August 2000 and December 1996, respectively. He obtained his B.Sc. degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh in November 1993. Professor Khan is currently supported by grants from the National Science Foundation (NSF), Texas Instruments, NOKIA, Alcatel, USA and has been awarded the Sun Equipment Grant. Dr. Khan has more than 50 articles, book chapters, and conference papers focusing in the areas of: database systems, multimedia information management, and data mining in bio-informatics and intrusion detection. Professor Khan has also served as a referee for database journals, conferences (e.g., IEEE TKDE, KAIS, ADL, VLDB) and he is currently serving as a program committee member for Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD2005), ACM Fourteenth Conference on Information and Knowledge Management (CIKM 2005), International Conference on Database and Expert Systems Applications DEXA 2005, and International Conference on Cooperative Information Systems (CoopIS 2005), and program chair of ACM SIGKDD International Workshop on Multimedia Data Mining, 2004. Dr. Balakrishnan Prabhakaran is currently with the Department of Computer Science, University of Texas at Dallas. Dr. B. Prabhakaran has been working in the area of multimedia systems: multimedia databases, authoring & presentation, resource management, and scalable web-based multimedia presentation servers. He has published several research papers in prestigious conferences and journals in this area.Dr. Prabhakaran received the NSF CAREER Award FY 2003 for his proposal on Animation Databases. Dr. Prabhakaran has served as an Associate Chair of the ACM Multimedia’2003 (November 2003, California), ACM MM 2000 (November 2000, Los Angeles), and ACM Multimedia’99 conference (Florida, November 1999). He has served as guest-editor (special issue on Multimedia Authoring and Presentation) for ACM Multimedia Systems journal. He is also serving on the editorial board of Multimedia Tools and Applications Journal, Kluwer Academic Publishers. He has also served as program committee member on several multimedia conferences and workshops. Dr. Prabhakaran has presented tutorials in several conferences on topics such as network resource management, adaptive multimedia presentations, and scalable multimedia servers.B. Prabhakaran has served as a visiting research faculty with the Department of Computer Science, University of Maryland, College Park. He also served as a faculty in the Department of Computer Science, National University of Singapore as well as in the Indian Institute of Technology, Madras, India     

Handicapping attacker's confidence: an alternative to <Emphasis Type="Italic">k</Emphasis>-anonymization

We present an approach of limiting the confidence of inferring sensitive properties to protect against the threats caused by data mining abilities. The problem has dual goals: preserve the information for a wanted data analysis request and limit the usefulness of unwanted sensitive inferences that may be derived from the release of data. Sensitive inferences are specified by a set of “privacy templates". Each template specifies the sensitive property to be protected, the attributes identifying a group of individuals, and a maximum threshold for the confidence of inferring the sensitive property given the identifying attributes. We show that suppressing the domain values monotonically decreases the maximum confidence of such sensitive inferences. Hence, we propose a data transformation that minimally suppresses the domain values in the data to satisfy the set of privacy templates. The transformed data is free of sensitive inferences even in the presence of data mining algorithms. The prior k-anonymization k has been italicized consistently throughout this article. focuses on personal identities. This work focuses on the association between personal identities and sensitive properties. Ke Wang received Ph.D. from Georgia Institute of Technology. He is currently a professor at School of Computing Science, Simon Fraser University. Before joining Simon Fraser, he was an associate professor at National University of Singapore. He has taught in the areas of database and data mining. Dr. Wang’s research interests include database technology, data mining and knowledge discovery, machine learning, and emerging applications, with recent interests focusing on the end use of data mining. This includes explicitly modeling the business goal (such as profit mining, bio-mining and web mining) and exploiting user prior knowledge (such as extracting unexpected patterns and actionable knowledge). He is interested in combining the strengths of various fields such as database, statistics, machine learning and optimization to provide actionable solutions to real-life problems. He is an associate editor of the IEEE TKDE journal and has served program committees for international conferences. Benjamin C. M. Fung received B.Sc. and M.Sc. degrees in computing science from Simon Fraser University. Received the postgraduate scholarship doctoral award from the Natural Sciences and Engineering Research Council of Canada (NSERC), Mr. Fung is currently a Ph.D. candidate at Simon Fraser. His recent research interests include privacy-preserving data mining, secure distributed computing, and text mining. Before pursuing his Ph.D., he worked in the R&D Department at Business Objects and designed reporting systems for various Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems, including BaaN, Siebel, and PeopleSoft. Mr. Fung has published in data engineering, data mining, and security conferences, journals, and books, including IEEE ICDE, IEEE ICDM, IEEE ISI, SDM, KAIS, and the Encyclopedia of Data Warehousing and Mining. Philip S. Yu received B.S. degree in E.E. from National Taiwan University, M.S. and Ph.D. degrees in E.E. from Stanford University, and M.B.A. degree from New York University. He is with IBM T.J. Watson Research Center and currently manager of the Software Tools and Techniques group. Dr. Yu has published more than 450 papers in refereed journals and conferences. He holds or has applied for more than 250 US patents. Dr. Yu is a Fellow of the ACM and the IEEE. He has received several IBM honors including two IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, two Research Division Awards and the 85th plateau of Invention Achievement Awards. He received a Research Contributions Award from IEEE International Conference on Data Mining in 2003 and also an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts” in 1999. Dr. Yu is an IBM Master Inventor.     

Efficient string matching with wildcards and length constraints

This paper defines a challenging problem of pattern matching between a pattern P and a text T, with wildcards and length constraints, and designs an efficient algorithm to return each pattern occurrence in an online manner. In this pattern matching problem, the user can specify the constraints on the number of wildcards between each two consecutive letters of P and the constraints on the length of each matching substring in T. We design a complete algorithm, SAIL that returns each matching substring of P in T as soon as it appears in T in an O(n+klmg) time with an O(lm) space overhead, where n is the length of T, k is the frequency of P's last letter occurring in T, l is the user-specified maximum length for each matching substring, m is the length of P, and g is the maximum difference between the user-specified maximum and minimum numbers of wildcards allowed between two consecutive letters in P.SAIL stands for string matching with wildcards and length constraints. Gong Chen received the B.Eng. degree from the Beijing University of Technology, China, and the M.Sc. degree from the University of Vermont, USA, both in computer science. He is currently a graduate student in the Department of Statistics at the University of California, Los Angeles, USA. His research interests include data mining, statistical learning, machine learning, algorithm analysis and design, and database management. Xindong Wu is a professor and the chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM and WWW, as well as 12 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM),an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He is the 2004 ACM SIGKDD Service Award winner. Xingquan Zhu received his Ph.D degree in Computer Science from Fudan University, Shanghai, China, in 2001. He spent 4 months with Microsoft Research Asia, Beijing, China, where he was working on content-based image retrieval with relevance feedback. From 2001 to 2002, he was a postdoctoral associate in the Department of Computer Science at Purdue University, West Lafayette, IN. He is currently a research assistant professor in the Department of Computer Science, the University of Vermont, Burlington, VT. His research interests include data mining, machine learning, data quality, multimedia computing, and information retrieval. Since 2000, Dr. Zhu has published extensively, including over 50 refereed papers in various journals and conference proceedings. Abdullah N. Arslan got his Ph.D. degree in Computer Science in 2002 from the University of California at Santa Barbara. Upon his graduation he joined the Department of Computer Science at the University of Vermont as an assistant professor. He has been with the computer science faculty there since then. Dr. Arslan's main research interests are on algorithms on strings, computational biology and bioinformatics. Dr. Arslan earned his Master's degree in Computer Science in 1996 from the University of North Texas, Denton, Texas and his Bachelor's degree in Computer Engineering in 1990 from the Middle East Technical University, Ankara, Turkey. He worked as a programmer for the Central Bank of Turkey between 1991 and 1994. Yu He received her B.E. degree in Information Engineering from Zhejiang University, China, in 2001. She is currently a graduate student in the Department of Computer Science at the University of Vermont. Her research interests include data mining, bioinformatics and pattern recognition.     

Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm can overcome the drawbacks of the one we presented in [7]. Shi-Jay Chen was born in 1972, in Taipei, Taiwan, Republic of China. He received the B.S. degree in information management from the Kaohsiung Polytechnic Institute, Kaohsiung, Taiwan, and the M.S. degree in information management from the Chaoyang University of Technology, Taichung, Taiwan, in 1997 and 1999, respectively. He received the Ph.D. degree at the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, in October 2004. His research interests include fuzzy systems, multicriteria fuzzy decisionmaking, and artificial intelligence. Shyi-Ming Chen was born on January 16, 1960, in Taipei, Taiwan, Republic of China. He received the Ph.D. degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in June 1991. From August 1987 to July 1989 and from August 1990 to July 1991, he was with the Department of Electronic Engineering, Fu-Jen University, Taipei, Taiwan. From August 1991 to July 1996, he was an Associate Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan. From August 1996 to July 1998, he was a Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan. From August 1998 to July 2001, he was a Professor in the Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. Since August 2001, he has been a Professor in the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He was a Visiting Scholar in the Department of Electrical Engineering and Computer Science, University of California, Berkeley, in 1999. He was a Visiting Scholar in the Institute of Information Science, Academia Sinica, Republic of China, in 2003. He has published more than 250 papers in referred journals, conference proceedings and book chapters. His research interests include fuzzy systems, information retrieval, knowledge-based systems, artificial intelligence, neural networks, data mining, and genetic algorithms. Dr. Chen has received several honors and awards, including the 1994 Outstanding Paper Award o f the Journal of Information and Education, the 1995 Outstanding Paper Award of the Computer Society of the Republic of China, the 1995 and 1996 Acer Dragon Thesis Awards for Outstanding M.S. Thesis Supervision, the 1995 Xerox Foundation Award for Outstanding M.S. Thesis Supervision, the 1996 Chinese Institute of Electrical Engineering Award for Outstanding M.S. Thesis Supervision, the 1997 National Science Council Award, Republic of China, for Outstanding Undergraduate Student's Project Supervision, the 1997 Outstanding Youth Electrical Engineer Award of the Chinese Institute of Electrical Engineering, Republic of China, the Best Paper Award of the 1999 National Computer Symposium, Republic of China, the 1999 Outstanding Paper Award of the Computer Society of the Republic of China, the 2001 Institute of Information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the 2001 Outstanding Talented Person Award, Republic of China, for the contributions in Information Technology, the 2002 Institute of information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the Outstanding Electrical Engineering Professor Award granted by the Chinese Institute of Electrical Engineering (CIEE), Republic of China, the 2002 Chinese Fuzzy Systems Association Best Thesis Award for Outstanding M.S. Thesis Supervision, the 2003 Outstanding Paper Award of the Technological and Vocational Education Society, Republic of China, the 2003 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision, the 2005 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 Taiwan Fuzzy Systems Association Award for Outstanding Ph.D. Dissertation Supervision, and the 2006 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision. Dr. Chen is currently the President of the Taiwanese Association for Artificial Intelligence (TAAI). He is a Senior Member of the IEEE, a member of the ACM, the International Fuzzy Systems Association (IFSA), and the Phi Tau Phi Scholastic Honor Society. He was an administrative committee member of the Chinese Fuzzy Systems Association (CFSA) from 1998 to 2004. He is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics - Part C, an Associate Editor of the IEEE Computational Intelligence Magazine, an Associate Editor of the Journal of Intelligent & Fuzzy Systems, an Editorial Board Member of the International Journal of Applied Intelligence, an Editor of the New Mathematics and Natural Computation Journal, an Associate Editor of the International Journal of Fuzzy Systems, an Editorial Board Member of the International Journal of Information and Communication Technology, an Editorial Board Member of the WSEAS Transactions on Systems, an Editor of the Journal of Advanced Computational Intelligence and Intelligent Informatics, an Associate Editor of the WSEAS Transactions on Computers, an Editorial Board Member of the International Journal of Computational Intelligence and Applications, an Editorial Board Member of the Advances in Fuzzy Sets and Systems Journal, an Editor of the International Journal of Soft Computing, an Editor of the Asian Journal of Information Technology, an Editorial Board Member of the International Journal of Intelligence Systems Technologies and Applications, an Editor of the Asian Journal of Information Management, an Associate Editor of the International Journal of Innovative Computing, Information and Control, and an Editorial Board Member of the International Journal of Computer Applications in Technology. He was an Editor of the Journal of the Chinese Grey System Association from 1998 to 2003. He is listed in International Who's Who of Professionals, Marquis Who's Who in the World, and Marquis Who's Who in Science and Engineering.     

Privacy preservation for data cubes

A range query finds the aggregated values over all selected cells of an online analytical processing (OLAP) data cube where the selection is specified by the ranges of contiguous values for each dimension. An important issue in reality is how to preserve the confidential information in individual data cells while still providing an accurate estimation of the original aggregated values for range queries. In this paper, we propose an effective solution, called the zero-sum method, to this problem. We derive theoretical formulas to analyse the performance of our method. Empirical experiments are also carried out by using analytical processing benchmark (APB) dataset from the OLAP Council. Various parameters, such as the privacy factor and the accuracy factor, have been considered and tested in the experiments. Finally, our experimental results show that there is a trade-off between privacy preservation and range query accuracy, and the zero-sum method has fulfilled three design goals: security, accuracy, and accessibility. Sam Y. Sung is an Associate Professor in the Department of Computer Science, School of Computing, National University of Singapore. He received a B.Sc. from the National Taiwan University in 1973, the M.Sc. and Ph.D. in computer science from the University of Minnesota in 1977 and 1983, respectively. He was with the University of Oklahoma and University of Memphis in the United States before joining the National University of Singapore. His research interests include information retrieval, data mining, pictorial databases and mobile computing. He has published more than 80 papers in various conferences and journals, including IEEE Transaction on Software Engineering, IEEE Transaction on Knowledge & Data Engineering, etc. Yao Liu received the B.E. degree in computer science and technology from Peking University in 1996 and the MS. degree from the Software Institute of the Chinese Science Academy in 1999. Currently, she is a Ph.D. candidate in the Department of Computer Science at the National University of Singapore. Her research interests include data warehousing, database security, data mining and high-speed networking. Hui Xiong received the B.E. degree in Automation from the University of Science and Technology of China, Hefei, China, in 1995, the M.S. degree in Computer Science from the National University of Singapore, Singapore, in 2000, and the Ph.D. degree in Computer Science from the University of Minnesota, Minneapolis, MN, USA, in 2005. He is currently an Assistant Professor of Computer Information Systems in the Management Science & Information Systems Department at Rutgers University, NJ, USA. His research interests include data mining, databases, and statistical computing with applications in bioinformatics, database security, and self-managing systems. He is a member of the IEEE Computer Society and the ACM. Peter A. Ng is currently the Chairperson and Professor of Computer Science at the University of Texas—Pan American. He received his Ph.D. from the University of Texas–Austin in 1974. Previously, he had served as the Vice President at the Fudan International Institute for Information Science and Technology, Shanghai, China, from 1999 to 2002, and the Executive Director for the Global e-Learning Project at the University of Nebraska at Omaha, 2000–2003. He was appointed as an Advisory Professor of Computer Science at Fudan University, Shanghai, China in 1999. His recent research focuses on document and information-based processing, retrieval and management. He has published many journal and conference articles in this area. He had served as the Editor-in-Chief for the Journal on Systems Integration (1991–2001) and as Advisory Editor for the Data and Knowledge Engineering Journal since 1989.     

Mining user access patterns with traversal constraint for predicting web page requests

The recent increase in HyperText Transfer Protocol (HTTP) traffic on the World Wide Web (WWW) has generated an enormous amount of log records on Web server databases. Applying Web mining techniques on these server log records can discover potentially useful patterns and reveal user access behaviors on the Web site. In this paper, we propose a new approach for mining user access patterns for predicting Web page requests, which consists of two steps. First, the Minimum Reaching Distance (MRD) algorithm is applied to find the distances between the Web pages. Second, the association rule mining technique is applied to form a set of predictive rules, and the MRD information is used to prune the results from the association rule mining process. Experimental results from a real Web data set show that our approach improved the performance over the existing Markov-model approach in precision, recall, and the reduction of user browsing time. Mei-Ling Shyu received her Ph.D. degree from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN in 1999, and three Master's degrees from Computer Science, Electrical Engineering, and Restaurant, Hotel, Institutional, and Tourism Management from Purdue University. She has been an Associate Professor in the Department of Electrical and Computer Engineering (ECE) at the University of Miami (UM), Coral Gables, FL, since June 2005, Prior to that, she was an Assistant Professor in ECE at UM dating from January 2000. Her research interests include data mining, multimedia database systems, multimedia networking, database systems, and security. She has authored and co-authored more than 120 technical papers published in various prestigious journals, refereed conference/symposium/workshop proceedings, and book chapters. She is/was the guest editor of several journal special issues. Choochart Haruechaiyasak received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of Miami, in 2003 with the Outstanding Departmental Graduating Student award from the College of Engineering. After receiving his degree, he has joined the National Electronics and Computer Technology Center (NECTEC), located in Thailand Science Park, as a researcher in Information Research and Development Division (RDI). His current research interests include data/ text/ Web mining, Natural Language Processing, Information Retrieval, Search Engines, and Recommender Systems. He is currently leading a small group of researchers and programmer to develop an open-source search engine for Thai language. One of his objectives is to promote the use of data mining technology and other advanced applications in Information Technology in Thailand. He is also a visiting lecturer for Data Mining, Artificial Intelligence and Decision Support Systems courses in many universities in Thailand. Shu-Ching Chen received his Ph.D. from the School of Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA in December, 1998. He also received Master's degrees in Computer Science, Electrical Engineering, and Civil Engineering from Purdue University. He has been an Associate Professor in the School of Computing and Information Sciences (SCIS), Florida International University (FIU) since August, 2004. Prior to that, he was an Assistant Professor in SCIS at FIU dating from August, 1999. His main research interests include distributed multimedia database systems and multimedia data mining. Dr. Chen has authored and co-authored more than 140 research papers in journals, refereed conference/symposium/workshop proceedings, and book chapters. In 2005, he was awarded the IEEE Systems, Man, and Cybernetics Society's Outstanding Contribution Award. He was also awarded a University Outstanding Faculty Research Award from FIU in 2004, Outstanding Faculty Service Award from SCIS in 2004 and Outstanding Faculty Research Award from SCIS in 2002.     

Supervised tensor learning

Tensor representation is helpful to reduce the small sample size problem in discriminative subspace selection. As pointed by this paper, this is mainly because the structure information of objects in computer vision research is a reasonable constraint to reduce the number of unknown parameters used to represent a learning model. Therefore, we apply this information to the vector-based learning and generalize the vector-based learning to the tensor-based learning as the supervised tensor learning (STL) framework, which accepts tensors as input. To obtain the solution of STL, the alternating projection optimization procedure is developed. The STL framework is a combination of the convex optimization and the operations in multilinear algebra. The tensor representation helps reduce the overfitting problem in vector-based learning. Based on STL and its alternating projection optimization procedure, we generalize support vector machines, minimax probability machine, Fisher discriminant analysis, and distance metric learning, to support tensor machines, tensor minimax probability machine, tensor Fisher discriminant analysis, and the multiple distance metrics learning, respectively. We also study the iterative procedure for feature extraction within STL. To examine the effectiveness of STL, we implement the tensor minimax probability machine for image classification. By comparing with minimax probability machine, the tensor version reduces the overfitting problem. We focus on the convex optimization-based binary classification learning algorithms in this paper. This is because the solution to a convex optimization-based learning algorithm is unique. Dacheng Tao received the B.Eng. degree from the University of Science and Technology of China (USTC), the MPhil degree from the Chinese University of Hong Kong (CUHK) and the PhD from the University of London (Birkbeck). He will join the Department of Computing in the Hong Kong Polytechnic University as an assistant professor. His research interests include biometric research, discriminant analysis, support vector machine, convex optimization for machine learning, multilinear algebra, multimedia information retrieval, data mining, and video surveillance. He published extensively at TPAMI, TKDE, TIP, TMM, TCSVT, CVPR, ICDM, ICASSP, ICIP, ICME, ACM Multimedia, ACM KDD, etc. He gained several Meritorious Awards from the Int’l Interdisciplinary Contest in Modeling, which is the highest level mathematical modeling contest in the world, organized by COMAP. He is a guest editor for special issues of the Int’l Journal of Image and Graphics (World Scientific) and the Neurocomputing (Elsevier). Xuelong Li works at the University of London. He has published in journals (IEEE T-PAMI, T-CSVT, T-IP, T-KDE, TMM, etc.) and conferences (IEEE CVPR, ICASSP, ICDM, etc.). He is an Associate Editor of IEEE T-SMC, Part C, Neurocomputing, IJIG (World Scientific), and Pattern Recognition (Elsevier). He is also an Editor Board Member of IJITDM (World Scientific) and ELCVIA (CVC Press). He is a Guest Editor for special issues of IJCM (Taylor and Francis), IJIG (World Scientific), and Neurocomputing (Elsevier). He co-chaired the 5th Annual UK Workshop on Computational Intelligence and the 6th the IEEE Int’l Conf. on Machine Learning and Cybernetics. He was also a publicity chair of the 7th IEEE Int’l Conf. on Data Mining and the 4th Int’l Conf. on Image and Graphics. He has been on the program committees of more than 50 conferences and workshops. Xindong Wu is a Professor and the Chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM, and WWW, as well as 12 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the Founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AIKP). He is the 2004 ACM SIGKDD Service Award winner. Weiming Hu received the Ph.D. degree from the Department of Computer Science and Engineering, Zhejiang University. From April 1998 to March 2000, he was a Postdoctoral Research Fellow with the Institute of Computer Science and Technology, Founder Research and Design Center, Peking University. Since April 1998, he has been with the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. Now he is a Professor and a Ph.D. Student Supervisor in the laboratory. His research interests are in visual surveillance, neural networks, filtering of Internet objectionable information, retrieval of multimedia, and understanding of Internet behaviors. He has published more than 80 papers on national and international journals, and international conferences. Stephen J. Maybank received a BA in Mathematics from King’s college, Cambridge in 1976 and a PhD in Computer Science from Birkbeck College, University of London in 1988. He was a research scientist at GEC from 1980 to 1995, first at MCCS, Frimley and then, from 1989, at the GEC Marconi Hirst Research Centre in London. In 1995 he became a lecturer in the Department of Computer Science at the University of Reading and in 2004 he became a professor in the School of Computer Science and Information Systems at Birkbeck College, University of London. His research interests include camera calibration, visual surveillance, tracking, filtering, applications of projective geometry to computer vision and applications of probability, statistics and information theory to computer vision. He is the author of more than 90 scientific publications and one book. He is a Fellow of the Institute of Mathematics and its Applications, a Fellow of the Royal Statistical Society and a Senior Member of the IEEE. For further information see http://www.dcs.bbk.ac.uk/~sjmaybank.     

Capabilities of outlier detection schemes in large datasets,framework and methodologies

Outlier detection is concerned with discovering exceptional behaviors of objects. Its theoretical principle and practical implementation lay a foundation for some important applications such as credit card fraud detection, discovering criminal behaviors in e-commerce, discovering computer intrusion, etc. In this paper, we first present a unified model for several existing outlier detection schemes, and propose a compatibility theory, which establishes a framework for describing the capabilities for various outlier formulation schemes in terms of matching users'intuitions. Under this framework, we show that the density-based scheme is more powerful than the distance-based scheme when a dataset contains patterns with diverse characteristics. The density-based scheme, however, is less effective when the patterns are of comparable densities with the outliers. We then introduce a connectivity-based scheme that improves the effectiveness of the density-based scheme when a pattern itself is of similar density as an outlier. We compare density-based and connectivity-based schemes in terms of their strengths and weaknesses, and demonstrate applications with different features where each of them is more effective than the other. Finally, connectivity-based and density-based schemes are comparatively evaluated on both real-life and synthetic datasets in terms of recall, precision, rank power and implementation-free metrics. Jian Tang received an MS degree from the University of Iowa in 1983, and PhD from the Pennsylvania State University in 1988, both from the Department of Computer Science. He joined the Department of Computer Science, Memorial University of Newfoundland, Canada, in 1988, where he is currently a professor. He has visited a number of research institutions to conduct researches ranging over a variety of topics relating to theories and practices for database management and systems. His current research interests include data mining, e-commerce, XML and bioinformatics. Zhixiang Chen is an associate professor in the Computer Science Department, University of Texas-Pan American. He received his PhD in computer science from Boston University in January 1996, BS and MS degrees in software engineering from Huazhong University of Science and Technology. He also studied at the University of Illinois at Chicago. He taught at Southwest State University from Fall 1995 to September 1997, and Huazhong University of Science and Technology from 1982 to 1990. His research interests include computational learning theory, algorithms and complexity, intelligent Web search, informational retrieval, and data mining. Ada Waichee Fu received her BSc degree in computer science in the Chinese University of Hong Kong in 1983, and both MSc and PhD degrees in computer science in Simon Fraser University of Canada in 1986, 1990, respectively; worked at Bell Northern Research in Ottawa, Canada, from 1989 to 1993 on a wide-area distributed database project; joined the Chinese University of Hong Kong in 1993. Her research interests are XML data, time series databases, data mining, content-based retrieval in multimedia databases, parallel, and distributed systems. David Wai-lok Cheung received the MSc and PhD degrees in computer science from Simon Fraser University, Canada, in 1985 and 1989, respectively. He also received the BSc degree in mathematics from the Chinese University of Hong Kong. From 1989 to 1993, he was a member of Scientific Staff at Bell Northern Research, Canada. Since 1994, he has been a faculty member of the Department of Computer Science in the University of Hong Kong. He is also the Director of the Center for E-Commerce Infrastructure Development. His research interests include data mining, data warehouse, XML technology for e-commerce and bioinformatics. Dr. Cheung was the Program Committee Chairman of the Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2001), Program Co-Chair of the Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2005). Dr. Cheung is a member of the ACM and the IEEE Computer Society.     

Tracking clusters in evolving data streams over sliding windows

Mining data streams poses great challenges due to the limited memory availability and real-time query response requirement. Clustering an evolving data stream is especially interesting because it captures not only the changing distribution of clusters but also the evolving behaviors of individual clusters. In this paper, we present a novel method for tracking the evolution of clusters over sliding windows. In our SWClustering algorithm, we combine the exponential histogram with the temporal cluster features, propose a novel data structure, the Exponential Histogram of Cluster Features (EHCF). The exponential histogram is used to handle the in-cluster evolution, and the temporal cluster features represent the change of the cluster distribution. Our approach has several advantages over existing methods: (1) the quality of the clusters is improved because the EHCF captures the distribution of recent records precisely; (2) compared with previous methods, the mechanism employed to adaptively maintain the in-cluster synopsis can track the cluster evolution better, while consuming much less memory; (3) the EHCF provides a flexible framework for analyzing the cluster evolution and tracking a specific cluster efficiently without interfering with other clusters, thus reducing the consumption of computing resources for data stream clustering. Both the theoretical analysis and extensive experiments show the effectiveness and efficiency of the proposed method. Aoying Zhou is currently a Professor in Computer Science at Fudan University, Shanghai, P.R. China. He won his Bachelor and Master degrees in Computer Science from Sichuan University in Chengdu, Sichuan, P.R. China in 1985 and 1988, respectively, and Ph.D. degree from Fudan University in 1993. He served as the member or chair of program committee for many international conferences such as WWW, SIGMOD, VLDB, EDBT, ICDCS, ER, DASFAA, PAKDD, WAIM, and etc. His papers have been published in ACM SIGMOD, VLDB, ICDE, and several other international journals. His research interests include Data mining and knowledge discovery, XML data management, Web mining and searching, data stream analysis and processing, peer-to-peer computing. Feng Cao is currently an R&D engineer in IBM China Research Laboratories. He received a B.E. degree from Xi'an Jiao Tong University, Xi'an, P.R. China, in 2000 and an M.E. degree from Huazhong University of Science and Technology, Wuhan, P.R. China, in 2003. From October 2004 to March 2005, he worked in Fudan-NUS Competency Center for Peer-to-Peer Computing, Singapore. In 2006, he received his Ph.D. degree from Fudan University, Shanghai, P.R. China. His current research interests include data mining and data stream. Weining Qian is currently an Assistant Professor in computer science at Fudan University, Shanghai, P.R. China. He received his M.S. and Ph.D. degree in computer science from Fudan University in 2001 and 2004, respectively. He is supported by Shanghai Rising-Star Program under Grant No. 04QMX1404 and National Natural Science Foundation of China (NSFC) under Grant No. 60673134. He served as the program committee member of several international conferences, including DASFAA 2006, 2007 and 2008, APWeb/WAIM 2007, INFOSCALE 2007, and ECDM 2007. His papers have been published in ICDE, SIAM DM, and CIKM. His research interests include data stream query processing and mining, and large-scale distributed computing for database applications. Cheqing Jin is currently an Assistant Professor in Computer Science at East China University of Science and Technology. He received his Bachelor and Master degrees in Computer Science from Zhejiang University in Hangzhou, P.R. China in 1999 and 2002, respectively, and the Ph.D. degree from Fudan University, Shanghai, P.R. China. He worked as a Research Assistant at E-business Technology Institute, the Hong Kong University from December 2003 to May 2004. His current research interests include data mining and data stream.     

Genetic operators for combinatorial optimization in TSP and microarray gene ordering

This paper deals with some new operators of genetic algorithms and demonstrates their effectiveness to the traveling salesman problem (TSP) and microarray gene ordering. The new operators developed are nearest fragment operator based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. While these result in faster convergence of Genetic Algorithm (GAs) in finding the optimal order of genes in microarray and cities in TSP, the nearest fragment operator can augment the search space quickly and thus obtain much better results compared to other heuristics. Appropriate number of fragments for the nearest fragment operator and appropriate substring length in terms of the number of cities/genes for the modified order crossover operator are determined systematically. Gene order provided by the proposed method is seen to be superior to other related methods based on GAs, neural networks and clustering in terms of biological scores computed using categorization of the genes. Shubhra Sankar Ray is a Visiting Research Fellow at the Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata, India. He received the M.Sc. in Electronic Science and M.Tech in Radiophysics & Electronics from University of Calcutta, Kolkata, India, in 2000 and 2002, respectively. Till March 2006, he had been a Senior Research Fellow of the Council of Scientific and Industrial Research (CSIR), New Delhi, India, working at Machine Intelligence Unit, Indian Statistical Institute, India. His research interests include bioinformatics, evolutionary computation, neural networks, and data mining. Sanghamitra Bandyopadhyay is an Associate Professor at Indian Statistical Institute, Calcutta, India. She did her Bachelors in Physics and Computer Science in 1988 and 1992 respectively. Subsequently, she did her Masters in Computer Science from Indian Institute of Technology (IIT), Kharagpur in 1994 and Ph.D in Computer Science from Indian Statistical Institute, Calcutta in 1998. She has worked in Los Alamos National Laboratory, Los Alamos, USA, in 1997, as a graduate research assistant, in the University of New South Wales, Sydney, Australia, in 1999, as a post doctoral fellow, in the Department of Computer Science and Engineering, University of Texas at Arlington, USA, in 2001 as a faculty and researcher, and in the Department of Computer Science and Engineering, University of Maryland Baltimore County, USA, in 2004 as a visiting research faculty. Dr. Bandyopadhyay is the first recipient of Dr. Shanker Dayal Sharma Gold Medal and Institute Silver Medal for being adjudged the best all round post graduate performer in IIT, Kharagpur in 1994. She has received the Indian National Science Academy (INSA) and the Indian Science Congress Association (ISCA) Young Scientist Awards in 2000, as well as the Indian National Academy of Engineering (INAE) Young Engineers' Award in 2002. She has published over ninety articles in international journals, conference and workshop proceedings, edited books and journal special issues and served as the Program Co-Chair of the 1st International Conference on Pattern Recognition and Machine Intelligence, 2005, Kolkata, India, and as the Tutorial Co-Chair, World Congress on Lateral Computing, 2004, Bangalore, India. She is on the editorial board of the International Journal on Computational Intelligence. Her research interests include Evolutionary and Soft Computation, Pattern Recognition, Data Mining, Bioinformatics, Parallel & Distributed Systems and VLSI. Sankar K. Pal (www.isical.ac.in/∼sankar) is the Director and Distinguished Scientist of the Indian Statistical Institute. He has founded the Machine Intelligence Unit, and the Center for Soft Computing Research: A National Facility in the Institute in Calcutta. He received a Ph.D. in Radio Physics and Electronics from the University of Calcutta in 1979, and another Ph.D. in Electrical Engineering along with DIC from Imperial College, University of London in 1982. He worked at the University of California, Berkeley and the University of Maryland, College Park in 1986-87; the NASA Johnson Space Center, Houston, Texas in 1990-92 & 1994; and in US Naval Research Laboratory, Washington DC in 2004. Since 1997 he has been serving as a Distinguished Visitor of IEEE Computer Society (USA) for the Asia-Pacific Region, and held seve ral visiting positions in Hong Kong and Australian universities. Prof. Pal is a Fellow of the IEEE, USA, Third World Academy of Sciences, Italy, International Association for Pattern recognition, USA, and all the four National Academies for Science/Engineering in India. He is a co-author of thirteen books and about three hundred research publications in the areas of Pattern Recognition and Machine Learning, Image Processing, Data Mining and Web Intelligence, Soft Computing, Neural Nets, Genetic Algorithms, Fuzzy Sets, Rough Sets, and Bioinformatics. He has received the 1990 S.S. Bhatnagar Prize (which is the most coveted award for a scientist in India), and many prestigious awards in India and abroad including the 1999 G.D. Birla Award, 1998 Om Bhasin Award, 1993 Jawaharlal Nehru Fellowship, 2000 Khwarizmi International Award from the Islamic Republic of Iran, 2000–2001 FICCI Award, 1993 Vikram Sarabhai Research Award, 1993 NASA Tech Brief Award (USA), 1994 IEEE Trans. Neural Networks Outstanding Paper Award (USA), 1995 NASA Patent Application Award (USA), 1997 IETE-R.L. Wadhwa Gold Medal, the 2001 INSA-S.H. Zaheer Medal, and 2005-06 P.C. Mahalanobis Birth Centenary Award (Gold Medal) for Lifetime Achievement . Prof. Pal is an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Trans. Neural Networks [1994–98, 2003–06], Pattern Recognition Letters, Neurocomputing (1995–2005), Applied Intelligence, Information Sciences, Fuzzy Sets and Systems, Fundamenta Informaticae, Int. J. Computational Intelligence and Applications, and Proc. INSA-A; a Member, Executive Advisory Editorial Board, IEEE Trans. Fuzzy Systems, Int. Journal on Image and Graphics, and Int. Journal of Approximate Reasoning; and a Guest Editor of IEEE Computer.