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.     

The Efficiency of Critical Slicing in Fault Localization

In software testing, developing effective debugging strategies is important to guarantee the reliability of software under testing. A heuristic technique is to cause failure and therefore expose faults. Based on this approach mutation testing has been found very useful technique in detecting faults. However, it suffers from two problems with successfully testing programs: (1) requires extensive computing resources and (2) puts heavy demand on human resources. Later, empirical observations suggest that critical slicing based on Statement Deletion (Sdl) mutation operator has been found the most effective technique in reducing effort and the required computing resources in locating the program faults. The second problem of mutation testing may be solved by automating the program testing with the help of software tools. Our study focuses on determining the effectiveness of the critical slicing technique with the help of the Mothra Mutation Testing System in detecting program faults. This paper presents the results showing the performance of Mothra Mutation Testing System through conducting critical slicing testing on a selected suite of programs. Zuhoor Abdullah Al-Khanjari is an assistant professor in the Computer Science Department at Sultan Qaboos University, Sultanate of Oman. She received her BSc in mathematics and computing from Sultan Qaboos University, MSc and PhD in Computer Science (Software Engineering) from the University of Liverpool, UK. Her research interests include software testing, database management, e-learning, human-computer interaction, programming languages, intelligent search engines, and web data mining and development. ~Currently, she is the coordinator of the software engineering research group in the Department of Computer Science, College of Science, Sultan Qaboos University. She is also coordinating a program to develop e-learning based undergraduate teaching in the Department of Computer Science. Currently she is holding the position of assistant dean for postgraduate studies and research in the College of Science, Sultan Qaboos University, Sultanate of Oman. Martin Woodward is a Senior Fellow in the Computer Science Department at the University of Liverpool in the UK. After obtaining BSc and Ph.D. degrees in mathematics from the University of Nottingham, he was employed by the University of Oxford as a Research Assistant on secondment to the UK Atomic Energy Authority at the Culham Laboratory. He has been at the University of Liverpool for many years and initially worked on the so-called ‘Testbed’ project, helping to develop automated tools for software testing which are now marketed successfully by a commercial organisation. His research interests include software testing techniques, the relationship between formal methods and testing, and software visualisation. He has served as Editor of the journal ‘Software Testing, Verification and Reliability’ for the past thirteen years. Haider Ramadhan is an associate professor in the Computer Science Department at Sultan Qaboos University. He received his BS and MS in Computer Science from University of North Carolina, and the PhD in Computer Science and AI from Sussex University. His research interests include visualization of software, systems, and process, system engineering, human-computer interaction, intelligent search engines, and Web data mining and development. Currently, he is the chairman of the Computer Science Department, College of Science, Sultan Qaboos University, Sultanate of Oman. Swamy Kutti (N. S. Kutti) is an associate professor in the Computer Science Department at Sultan Qaboos University. He received his B.E. in Electronics Engineering from the University of Madras, M.E. in Communication Engineering from Indian Institute of Science (Bangalore), and the MSc in Computer Science from Monash University (Australia) and PhD in Computer Science from Deakin University (Australia). His research interests include Real-Time Programming, Programming Languages, Program Testing and Verification, eLearning, and Distributed Operating Systems.     

ScaleSpaceViz: α-Scale spaces in practice

Kernels of the so-called α-scale space have the undesirable property of having no closed-form representation in the spatial domain, despite their simple closed-form expression in the Fourier domain. This obstructs spatial convolution or recursive implementation. For this reason an approximation of the 2D α-kernel in the spatial domain is presented using the well-known Gaussian kernel and the Poisson kernel. Experiments show good results, with maximum relative errors of less than 2.4%. The approximation has been successfully implemented in a program for visualizing α-scale spaces. Some examples of practical applications with scale space feature points using the proposed approximation are given. The text was submitted by the authors in English. Frans Kanters received his MSc degree in Electrical Engineering in 2002 from the Eindhoven University of Technology in the Netherlands. Currently he is working on his PhD at the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. His PhD work is part of the “Deep Structure, Singularities, and Computer Vision (DSSCV)” project sponsored by the European Union. His research interests include scale space theory, image reconstruction, image processing algorithms, and hardware implementations thereof. Luc Florack received his MSc degree in theoretical physics in 1989 and his PhD degree cum laude in 1993 with a thesis on image structure, both from Utrecht University, the Netherlands. During the period from 1994 to 1995, he was an ERCIM/HCM research fellow at INRIA Sophia-Antipolis, France, and IN-ESC Aveiro, Portugal. In 1996 he was an assistant research professor at DIKU, Copenhagen, Denmark, on a grant from the Danish Research Council. From 1997 to June 2001, he was an assistant research professor at Utrecht University in the Department of Mathematics and Computer Science. Since June 1, 2001, he has been working as an assistant professor and, then, as an associate professor at Eindhoven University of Technology, Department of Biomedical Engineering. His interest includes all multiscale structural aspects of signals, images, and movies and their applications to imaging and vision. Remco Duits received his MSc degree (cum laude) in Mathematics in 2001 from the Eindhoven University of Technology, the Netherlands. Today he is a PhD student at the Department of Biomedical Engineering at the Eindhoven University of Technology on the subject of multiscale perceptual organization. His interest subtends functional analysis, group theory, partial differential equations, multiscale representations and their applications to biomedical imaging and vision, perceptual grouping. Currently, he is finishing his thesis “Perceptual Organization in Image Analysis (A Mathematical Approach Based on Scale, Orientation and Curvature).” During his PhD work, several of his submissions at conferences were chosen as selected or best papers—in particular, at the PRIA 2004 conference on pattern recognition and image analysis in St. Petersburg, where he received a best paper award (second place) for his work on invertible orientation scores. Bram Platel received his Masters Degree cum laude in biomedical engineering from the Eindhoven University of Technology in 2002. His research interests include image matching, scale space theory, catastrophe theory, and image-describing graph constructions. Currently he is working on his PhD in the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. Bart M. ter Haar Romany is full professor in Biomedical Image Analysis at the Department of Biomedical Engineering at Eindhoven University of Technology. He has been in this position since 2001. He received a MSc in Applied Physics from Delft University of Technology in 1978, and a PhD on neuromuscular nonlinearities from Utrecht University in 1983. After being the principal physicist of the Utrecht University Hospital Radiology Department, in 1989 he joined the department of Medical Imaging at Utrecht University as an associate professor. His interests are mathematical aspects of visual perception, in particular linear and non-linear scale-space theory, computer vision applications, and all aspects of medical imaging. He is author of numerous papers and book chapters on these issues; he edited a book on non-linear diffusion theory and is author of an interactive tutorial book on scale-space theory in computer vision. He has initiated a number of international collaborations on these subjects. He is an active teacher in international courses, a senior member of IEEE, and IEEE Chapter Tutorial Speaker. He is chairman of the Dutch Biophysical Society.     

Towards integrating semantics of multi-media resources and processes in e-Learning

Internet-based e-Learning has experienced a boom and bust situation in the past 10 years [32]. To bring in new forces to knowledge-oriented e-Learning, this paper addresses the semantic integration issue of multi-media resources and learning processes with theoretical learning supports in an integrated framework. This paper proposes a context-mediated approach that aims to enable semantic-based inter-operations across knowledge domains, even across the WWW and the Semantic Web [8]. The proposed semantic e-Learning framework enables intelligent operations of heterogeneous multi-media contents based on a generic semantic context intermediation model. This framework supports intelligent e-Learning with a knowledge network for knowledge object visualization, an enhanced Kolb's learning cycle [31] to guide learning practices, and a learning health care framework for personalized learning. W. Huang received his PhD in Computer Science from Nanjing University in 2001, MEng in Pattern Recognition and Intelligent Control and BEng in Automatic Control from Southeast University in 1998 and 1995, respectively. Dr. Huang is currently a senior lecturer with the Faculty of Computing, Information Systems and Mathematics at Kingston University London. Prior to this, he was a lecturer with the Centre for Internet Computing, The University of Hull, United Kingdom. Between October 2001 and September 2002, Dr. Huang was a post-doctoral research fellow at the University Lyon 1, France. His research interests include knowledge engineering and management, adaptive multimedia service, and pragmatic Semantic Web supporting technologies. His recent research focuses on semantic context aware computing and its applications in intelligent e-Services such as e-Learning and e-Enterprises. Dr. Huang is a member of ACM and IEEE Computer Society. E. Eze received his BS Degree in Computer Science from University of Nigeria in 1999. He is now a PhD student with the Centre for Internet Computing, The University of Hull, United Kingdom. His research interests include multimedia semantic modelling and representation and contextual knowledge engineering. D. Webster is currently studying for a PhD in Computer Science on the topic of trusted agents in the Semantic Web. He holds a 2-1 honours degree in Internet Computing from the University of Hull, UK and is a member of the British Computer Society. In addition to Web-based research, he also has an interest in graphics and has been involved in the development of graphics engines for video game projects on embedded and personal computing platforms.     

Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance

In this paper, we identify a new task for studying the outlying degree (OD) of high-dimensional data, i.e. finding the subspaces (subsets of features) in which the given points are outliers, which are called their outlying subspaces. Since the state-of-the-art outlier detection techniques fail to handle this new problem, we propose a novel detection algorithm, called High-Dimension Outlying subspace Detection (HighDOD), to detect the outlying subspaces of high-dimensional data efficiently. The intuitive idea of HighDOD is that we measure the OD of the point using the sum of distances between this point and itsknearest neighbors. Two heuristic pruning strategies are proposed to realize fast pruning in the subspace search and an efficient dynamic subspace search method with a sample-based learning process has been implemented. Experimental results show that HighDOD is efficient and outperforms other searching alternatives such as the naive top–down, bottom–up and random search methods, and the existing outlier detection methods cannot fulfill this new task effectively. Ji Zhang received his BS from Department of Information Systems and Information Management at Southeast University, Nanjing, China, in 2000 and MSc from Department of Computer Science at National University of Singapore in 2002. He worked as a researcher in Center for Information Mining and Extraction (CHIME) at National University of Singapore from 2002 to 2003 and Department of Computer Science at University of Toronto from 2003 to 2005. He is currently with Faculty of Computer Science at Dalhousie University, Canada. His research interests include Knowledge Discovery and Data Mining, XML and Data Cleaning. He has published papers in Journal of Intelligent Information Systems (JIIS), Journal of Database Management (JDM), and major international conferences such as VLDB, WWW, DEXA, DaWaK, SDM, and so on. Hai Wang is an assistant professor in the Department of Finance Management Science at Sobey School of Business of Saint Mary's University, Canada. He received his BSc in computer science from the University of New Brunswick, and his MSc and PhD in Computer Science from the University of Toronto. His research interests are in the areas of database management, data mining, e-commerce, and performance evaluation. His papers have been published in International Journal of Mobile Communications, Data Knowledge Engineering, ACM SIGMETRICS Performance Evaluation Review, Knowledge and Information Systems, Performance Evaluation, and others.     

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.     

Chance discovery and scenario analysis

Scenario analysis is often used to identify possible chance events. However, no formal, computational theory yet exists for scenario analysis. In this paper, we commence development of such a theory by defining a scenario in an argumentation context, and by considering the question of when two scenarios are the same. Peter McBurney, Ph.D.: He is a lecturer in the Department of Computer Science at the University of Liverpool, UK. He has a first degree in Pure Mathematics and Statistics from the Australian National University, Canberra, and a Ph.D in Artificial Intelligence from the University of Liverpool. His Ph.D research concerned the design of protocols for rational interaction between autonomous software agents, and he has several publications in this area. Prior to completing his Ph.D he worked as a consultant to major telecommunications network operating companies, primarily in mobile and satellite communications, where his work involved strategic marketing programming. Simon Parsons, Ph.D.: He is currently visiting the Sloan School of Management at Massachusetts Institute of Technology (MIT) and is a Visiting Professor at the University of Liverpool, UK. He holds a first degree in Engineering from Cambridge University, and an MSc and Ph.D in Artificial Intelligence from the University of London. In 1998, he was awarded the Young Engineer Achievement Medal of the British Institution of Electrical Engineers (IEE), the largest professional engineering society in Europe. He has published 4 books and over 100 articles on autonomous agents and multi-agent systems, uncertainty formalisms, risk and decision-making.     

Singular value decomposition based data distortion strategy for privacy protection

Privacy-preserving is a major concern in the application of data mining techniques to datasets containing personal, sensitive, or confidential information. Data distortion is a critical component to preserve privacy in security-related data mining applications, such as in data mining-based terrorist analysis systems. We propose a sparsified Singular Value Decomposition (SVD) method for data distortion. We also put forth a few metrics to measure the difference between the distorted dataset and the original dataset and the degree of the privacy protection. Our experimental results using synthetic and real world datasets show that the sparsified SVD method works well in preserving privacy as well as maintaining utility of the datasets. Shuting Xu received her PhD in Computer Science from the University of Kentucky in 2005. Dr. Xu is presently an Assistant Professor in the Department of Computer Information Systems at the Virginia State University. Her research interests include data mining and information retrieval, database systems, parallel, and distributed computing. Jun Zhang received a PhD from The George Washington University in 1997. He is an Associate Professor of Computer Science and Director of the Laboratory for High Performance Scientific Computing & Computer Simulation and Laboratory for Computational Medical Imaging & Data Analysis at the University of Kentucky. His research interests include computational neuroinformatics, data miningand information retrieval, large scale parallel and scientific computing, numerical simulation, iterative and preconditioning techniques for large scale matrix computation. Dr. Zhang is associate editor and on the editorial boards of four international journals in computer simulation andcomputational mathematics, and is on the program committees of a few international conferences. His research work has been funded by the U.S. National Science Foundation and the Department of Energy. He is recipient of the U.S. National Science Foundation CAREER Award and several other awards. Dianwei Han received an M.E. degree from Beijing Institute of Technology, Beijing, China, in 1995. From 1995to 1998, he worked in a Hitachi company(BHH) in Beijing, China. He received an MS degree from Lamar University, USA, in 2003. He is currently a PhD student in the Department of Computer Science, University of Kentucky, USA. His research interests include data mining and information retrieval, computational medical imaging analysis, and artificial intelligence. Jie Wang received the masters degree in Industrial Automation from Beijing University of Chemical Technology in 1996. She is currently a PhD student and a member of the Laboratory for High Performance Computing and Computer Simulation in the Department of Computer Science at the University of Kentucky, USA. Her research interests include data mining and knowledge discovery, information filtering and retrieval, inter-organizational collaboration mechanism, and intelligent e-Technology.     

Information access in multimedia databases based on feature models

With the increasing popularity of the WWW, the main challenge in computer science has become content-based retrieval of multimedia objects. Access to multimedia objects in databases has long been limited to the information provided in manually assigned keywords. Now, with the integration of feature-detection algorithms in database systems software, content-based retrieval can be fully integrated with query processing. We describe our experimentation platform under development, making database technology available to multimedia. Our approach is based on the new notion of feature databases. Its architecture fully integrates traditional query processing and content-based retrieval techniques. Arjen P. de Vries, Ph.D.: He received his Ph.D. in Computer Science from the University of Twente in 1999, on the integration of content management in database systems. He is especially interested in the new requirements on the design of database systems to support content-based retrieval in multimedia digital libraries. He has continued to work on multimedia database systems as a postdoc at the CWI in Amsterdam as well as University of Twente. Menzo Windhouwer: He received his MSc in Computer Science and Management from the University of Amsterdam in 1997. Currently he is working in the CWI Database Research Group on his Ph.D., which is concerned with multimedia indexing and retrieval using feature grammars. Peter M.G. Apers, Ph.D.: He is a full professor in the area of databases at the University of Twente, the Netherlands. He obtained his MSc and Ph.D. at the Free University, Amsterdam, and has been a visiting researcher at the University of California, Santa Cruz and Stanford University. His research interests are query optimization in parallel and distributed database systems to support new application domains, such as multimedia applications and WWW. He has served on the program committees of major database conferences: VLDB, SIGMOD, ICDE, EDBT. In 1996 he was the chairman of the EDBT PC. In 2001 he will, for the second time, be the chairman of the European PC of the VLDB. Currently he is coordinating Editor-in-Chief of the VLDB Journal, editor of Data & Knowledge Engineering, and editor of Distributed and Parallel Databases. Martin Kersten, Ph.D.: He received his PhD in Computer Science from the Vrije Universiteit in 1985 on research in database security, whereafter he moved to CWI to establish the Database Research Group. Since 1994 he is professor at the University of Amsterdam. Currently he is heading a department involving 60 researchers in areas covering BDMS architectures, datamining, multimedia information systems, and quantum computing. In 1995 he co-founded Data Distilleries, specialized in data mining technology, and became a non-executive board member of the software company Consultdata Nederland. He has published ca. 130 scientific papers and is member of the editorial board of VLDB journal and Parallel and Distributed Systems. He acts as a reviewer for ESPRIT projects and is a trustee of the VLDB Endowment board.     

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.     

Constraining and summarizing association rules in medical data

Association rules are a data mining technique used to discover frequent patterns in a data set. In this work, association rules are used in the medical domain, where data sets are generally high dimensional and small. The chief disadvantage about mining association rules in a high dimensional data set is the huge number of patterns that are discovered, most of which are irrelevant or redundant. Several constraints are proposed for filtering purposes, since our aim is to discover only significant association rules and accelerate the search process. A greedy algorithm is introduced to compute rule covers in order to summarize rules having the same consequent. The significance of association rules is evaluated using three metrics: support, confidence and lift. Experiments focus on discovering association rules on a real data set to predict absence or existence of heart disease. Constraints are shown to significantly reduce the number of discovered rules and improve running time. Rule covers summarize a large number of rules by producing a succinct set of rules with high-quality metrics. Carlos Ordonez received a degree in applied mathematics (actuarial sciences) and an MS degree in computer science, both from the UNAM University, Mexico, in 1992 and 1996, respectively. He got a PhD degree in computer science from the Georgia Institute of Technology, USA, in 2000. Dr. Ordonez currently works for Teradata (NCR) conducting research on database and data mining technology. He has published more than 20 research articles and holds three patents. Norberto Ezquerra obtained his undergraduate degree in mathematics and physics from the University of South Florida, and his doctoral degree from Florida State University, USA. He is an associate professor at the College of Computing at the Georgia Institute of Technology and an adjunct faculty member in the Emory University School of Medicine. His research interests include computer graphics, computer vision in medicine, AI in medicine, modeling of physically based systems, medical informatics and telemedicine. He is associate editor of the IEEE Transactions on Medical Imaging Journal, and a member of the American Medical Informatics Association and the IEEE Engineering in Medicine Biology Society. Cesar A. Santana received his MD degree in 1984 from the Institute of Medical Science, in Havana, Cuba. In 1988, he finished his residency training in internal medicine, and in 1991, completed a fellowship in nuclear medicine in Havana, Cuba. Dr. Santana received a PhD in nuclear cardiology in 1996 from the Department of Cardiology of the Vall d' Hebron University Hospital in Barcelona, Spain. Dr. Santana is an assistant professor at the Emory University School of Medicine and conducts research in the Radiology Department at the Emory University Hospital.     

A productivity benchmarking case study using Bayesian credible intervals

A productivity benchmarking case study is presented. Empirically valid evidence exists to suggest that certain project factors, such as development type and language type, influence project effort and productivity and a comparison is made taking into account these and other factors. The case study identifies a reasonably comparable set of data that was taken from a large benchmarking data repository by using the factors. This data set was then compared with the small data set presented by a company for benchmarking. The study illustrates how productivity rates might be misleading unless these factors are taken into account. Further, rather than simply giving a ratio for the company's productivity performance against the benchmark, the study shows how confidence about the company's performance can be expressed in terms of Bayesian confidence (credible) intervals for the ratio of the arithmetic means of the two data sets. John Moses is a Senior Lecturer in the School of Computing at the University of Sunderland and has received a BSc. degree in Applied Mathematics and Computing Science (Hons.) from the University of Sheffield, an MSc. in Operational Research (White Fish Authority prize in O.R.) from the University of Hull and a PhD. in Computer Science from the University of Sunderland in 1997. He has over seven years experience in commercial computing, working as a systems and operational research analyst in the steel, water, plastics and printing industries. John Moses has twenty years experience teaching and has lectured at the Universities of Teesside, Humberside and Sunderland. He is a member of the IEEE Computer Society, the British Computer Society and the Operational Research Society. His research interests are in software measurement and prediction systems for software development. Malcolm Farrow graduated in 1976 with first class honours in Statistics at the University of Newcastle upon Tyne. He then had an industrial studentship at the University of Newcastle upon Tyne with Alcan Lynemouth Ltd and got his PhD in 1979 for work on time series problems associated with the control of aluminium reduction cells. From 1979 he was a lecturer at the University of Hull, moving to Leicester Polytechnic (now De Montfort University) in 1981 and then Sunderland in 1984. Dr. Farrow became a Chartered Statistician in 1993 when this qualification was introduced by the Royal Statistical Society. In 2005 he became a Senior Lecturer in Statistics at the University of Newcastle upon Tyne. Norman Parrington graduated from Kings College, University of London with a degree in history. This naturally led him into a career as a computer programmer and systems analyst with the UK Government. He progressed to a Masters in Computing Science at the then North Staffordshire Polytechnic (now Staffordshire University) and thereafter worked for the UK Government procurement Agency as a compiler and operating system specialist. After three years Norman joined the UK Civil service as a Lecturer and three years later joined the University of Sunderland (then Sunderland polytechnic) where he has been for 22 years, currently occupying the post of Associate Dean where his responsibilities include Postgraduate programme Development. Normans major academic interests lie in the areas of software testing and software tools he has had more than 50 journal and conference papers published in these areas. He has co-authored two books “Understanding Software Testing” and “IT Training in Singapore” Professor Peter Smith is Dean of the School of Computing and Technology at the University of Sunderland, UK. Peter joined the University of Sunderland (then Sunderland Polytechnic) as a student in 1975. He graduated in 1978 with a BSc (Hons) Combined Studies in Science, in the subjects Mathematics and Computing. After graduating, he stayed in the University to study for a PhD in Computer Simulation. He then joined the staff of the Polytechnic as a Lecturer in Computing. His research interests are in the areas of expert systems, software engineering and computers in manufacturing and he has published over 200 papers on these subjects. He is particularly interested in developing novel techniques which can be applied for the solution of real business and industrial problems. He is also author of three textbooks on Knowledge Engineering and Software Engineering. He is a Fellow of the British Computer Society, a Chartered Engineer, a Chartered Mathematician and a Fellow of the Institute of Mathematics and its Applications. Peter has spoken at conferences around the world, including several invited conference addresses. He has also managed a large number of collaborative research projects, funded by the European Union, the UK Department of Trade and Industry and several industrial partners.     

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.     

Multiple labels associative classification

Building fast and accurate classifiers for large-scale databases is an important task in data mining. There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. In this paper, the problem of producing rules with multiple labels is investigated, and we propose a multi-class, multi-label associative classification approach (MMAC). In addition, four measures are presented in this paper for evaluating the accuracy of classification approaches to a wide range of traditional and multi-label classification problems. Results for 19 different data sets from the UCI data collection and nine hyperheuristic scheduling runs show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches. Fadi Abdeljaber Thabtah received a B.S. degree in Computer Science from Philadelphia University, Jordan, in 1997 and an M.S. degree in Computer Science from California State University, USA in 2001. From 1996 to 2001, he worked as professional in database programming and administration in United Insurance Ltd. in Amman. In 2002, he started his academic career and joined the Philadelphia University as a lecturer. He is currently a final graduate student at the Department of Computer Science, Bradford University, UK. He has published about seven scientific papers in the areas of data mining and machine learning. His research interests include machine learning, data mining, artificial intelligence and object-oriented databases. Peter Cowling is a Professor of Computing at the University of Bradford. He obtained M.A. and D.Phil. degrees from the University of Oxford. He leads the Modelling Optimisation Scheduling And Intelligent Control (MOSAIC) research centre (http://mosaic.ac), whose main research interests lie in the investigation and development of new modelling, optimisation, control and decision support technologies, which bridge the gap between theory and practice. Applications include production and personnel scheduling, intelligent game agents and data mining. He has published over 40 scientific papers in these areas and is active as a consultant to industry. Yonghong Peng's research areas include machine learning and data mining, and bioinformatics. He has published more than 35 scientific papers in related areas. Dr. Peng is a member of the IEEE and Computer Society, and has been a member of the programme committee of several conferences and workshops. Dr. Peng referees papers for several journals including the IEEE Trans. on Systems, Man and Cybernetics (part C), IEEE Trans. on Evolutionary Computation, Journal of Fuzzy Sets and Systems, Journal of Bioinformatics, and Journal of Data Mining and Knowledge Discovery, and is refereeing papers for several conferences.     

A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches. Cane Wing-ki Leung is a PhD student in the Department of Computing, The Hong Kong Polytechnic University, where she received her BA degree in Computing in 2003. Her research interests include collaborative filtering, data mining and computer-supported collaborative work. Stephen Chi-fai Chan is an Associate Professor and Associate Head of the Department of Computing, The Hong Kong Polytechnic University. Dr. Chan received his PhD from the University of Rochester, USA, worked on computer-aided design at Neo-Visuals, Inc. in Toronto, Canada, and researched in computer-integrated manufacturing at the National Research Council of Canada before joining the Hong Kong Polytechnic University in 1993. He is currently working on the development of collaborative Web-based information systems, with applications in education, electronic commerce, and manufacturing. Fu-lai Chung received his BSc degree from the University of Manitoba, Canada, in 1987, and his MPhil and PhD degrees from the Chinese University of Hong Kong in 1991 and 1995, respectively. He joined the Department of Computing, Hong Kong Polytechnic University in 1994, where he is currently an Associate Professor. He has published widely in the areas of computational intelligence, pattern recognition and recently data mining and multimedia in international journals and conferences and his current research interests include time series data mining, Web data mining, bioinformatics data mining, multimedia content analysis,and new computational intelligence techniques.     

A metamodel for modeling system features and their refinement, constraint and interaction relationships

This paper presents a metamodel for modeling system features and relationships between features. The underlying idea of this metamodel is to employ features as first-class entities in the problem space of software and to improve the customization of software by explicitly specifying both static and dynamic dependencies between system features. In this metamodel, features are organized as hierarchy structures by the refinement relationships, static dependencies between features are specified by the constraint relationships, and dynamic dependencies between features are captured by the interaction relationships. A first-order logic based method is proposed to formalize constraints and to verify constraints and customization. This paper also presents a framework for interaction classification, and an informal mapping between interactions and constraints through constraint semantics. Hong Mei received the BSc and MSc degrees in computer science from the Nanjing University of Aeronautics and Astronautics (NUAA), China, in 1984 and 1987, respectively, and the PhD degree in computer science from the Shanghai Jiao Tong University in 1992. He is currently a professor of Computer Science at the Peking University, China. His current research interests include Software Engineering and Software Engineering Environment, Software Reuse and Software Component Technology, Distributed Object Technology, and Programming Language. He has published more than 100 technical papers. Wei Zhang received the BSc in Engineering Thermophysics and the MSc in Computer Science from the Nanjing University of Aeronautics and Astronautics (NUAA), China, in 1999 and 2002, respectively. He is currently a PhD student at the School of Electronics Engineering and Computer Science of the Peking University, China. His research interests include feature-oriented requirements modeling, feature-driven software architecture design and feature-oriented software reuse. Haiyan Zhao received both the BSc and the MSc degree in Computer Science from the Peking Univeristy, China, and the Ph.D degree in Information Engineering from the University of Tokyo, Japan. She is currently an associate professor of Computer Science at the Peking University, China. Her research interests include Software Reuse, Domain Engineering, Domain Specific Languange and Program Transformation.     

On High Dimensional Projected Clustering of Data Streams

The data stream problem has been studied extensively in recent years, because of the great ease in collection of stream data. The nature of stream data makes it essential to use algorithms which require only one pass over the data. Recently, single-scan, stream analysis methods have been proposed in this context. However, a lot of stream data is high-dimensional in nature. High-dimensional data is inherently more complex in clustering, classification, and similarity search. Recent research discusses methods for projected clustering over high-dimensional data sets. This method is however difficult to generalize to data streams because of the complexity of the method and the large volume of the data streams.In this paper, we propose a new, high-dimensional, projected data stream clustering method, called HPStream. The method incorporates a fading cluster structure, and the projection based clustering methodology. It is incrementally updatable and is highly scalable on both the number of dimensions and the size of the data streams, and it achieves better clustering quality in comparison with the previous stream clustering methods. Our performance study with both real and synthetic data sets demonstrates the efficiency and effectiveness of our proposed framework and implementation methods.Charu C. Aggarwal received his B.Tech. degree in Computer Science from the Indian Institute of Technology (1993) and his Ph.D. degree in Operations Research from the Massachusetts Institute of Technology (1996). He has been a Research Staff Member at the IBM T. J. Watson Research Center since June 1996. He has applied for or been granted over 50 US patents, and has published over 75 papers in numerous international conferences and journals. He has twice been designated Master Inventor at IBM Research in 2000 and 2003 for the commercial value of his patents. His contributions to the Epispire project on real time attack detection were awarded the IBM Corporate Award for Environmental Excellence in 2003. He has been a program chair of the DMKD 2003, chair for all workshops organized in conjunction with ACM KDD 2003, and is also an associate editor of the IEEE Transactions on Knowledge and Data Engineering Journal. His current research interests include algorithms, data mining, privacy, and information retrieval.Jiawei Han is a Professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. He has been working on research into data mining, data warehousing, stream and RFID data mining, spatiotemporal and multimedia data mining, biological data mining, social network analysis, text and Web mining, and software bug mining, with over 300 conference and journal publications. He has chaired or served in many program committees of international conferences and workshops, including ACM SIGKDD Conferences (2001 best paper award chair, 1996 PC co-chair), SIAM-Data Mining Conferences (2001 and 2002 PC co-chair), ACM SIGMOD Conferences (2000 exhibit program chair), International Conferences on Data Engineering (2004 and 2002 PC vice-chair), and International Conferences on Data Mining (2005 PC co-chair). He also served or is serving on the editorial boards for Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Journal of Computer Science and Technology, and Journal of Intelligent Information Systems. He is currently serving on the Board of Directors for the Executive Committee of ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). Jiawei has received three IBM Faculty Awards, the Outstanding Contribution Award at the 2002 International Conference on Data Mining, ACM Service Award (1999) and ACM SIGKDD Innovation Award (2004). He is an ACM Fellow (since 2003). He is the first author of the textbook “Data Mining: Concepts and Techniques” (Morgan Kaufmann, 2001).Jianyong Wang received the Ph.D. degree in computer science in 1999 from the Institute of Computing Technology, the Chinese Academy of Sciences. Since then, he ever worked as an assistant professor in the Department of Computer Science and Technology, Peking (Beijing) University in the areas of distributed systems and Web search engines (May 1999–May 2001), and visited the School of Computing Science at Simon Fraser University (June 2001–December 2001), the Department of Computer Science at the University of Illinois at Urbana-Champaign (December 2001–July 2003), and the Digital Technology Center and Department of Computer Science and Engineering at the University of Minnesota (July 2003–November 2004), mainly working in the area of data mining. He is currently an associate professor in the Department of Computer Science and Technology, Tsinghua University, Beijing, China.Philip S. Yuis the manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. The current focuses of the project include the development of advanced algorithms and optimization techniques for data mining, anomaly detection and personalization, and the enabling of Web technologies to facilitate E-commerce and pervasive computing. Dr. Yu,s research interests include data mining, Internet applications and technologies, database systems, multimedia systems, parallel and distributed processing, disk arrays, computer architecture, performance modeling and workload analysis. Dr. Yu has published more than 340 papers in refereed journals and conferences. He holds or has applied for more than 200 US patents. Dr. Yu is an IBM Master Inventor.Dr. Yu is a Fellow of the ACM and a Fellow of the IEEE. He will become the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering on Jan. 2001. He is an associate editor of ACM Transactions of the Internet Technology and also Knowledge and Information Systems Journal. He is a member of the IEEE Data Engineering steering committee. He also serves on the steering committee of IEEE Intl. Conference on Data Mining. He received an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts”. Philip S. Yu received the B.S. Degree in E.E. from National Taiwan University, Taipei, Taiwan, the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University.     

Call classification using recurrent neural networks, support vector machines and finite state automata

Our objective is spoken-language classification for helpdesk call routing using a scanning understanding and intelligent-system techniques. In particular, we examine simple recurrent networks, support-vector machines and finite-state transducers for their potential in this spoken-language-classification task and we describe an approach to classification of recorded operator-assistance telephone utterances. The main contribution of the paper is a comparison of a variety of techniques in the domain of call routing. Support-vector machines and transducers are shown to have some potential for spoken-language classification, but the performance of the neural networks indicates that a simple recurrent network performs best for helpdesk call routing. Sheila Garfield received a BSc (Hons) in computing from the University of Sunderland in 2000 where, as part of her programme of study, she completed a project associated with aphasic language processing. She received her PhD from the same university, in 2004, for a programme of work connected with hybrid intelligent systems and spoken-language processing. In her PhD thesis, she collaborated with British Telecom and suggested a novel hybrid system for call routing. Her research interests are natural language processing, hybrid systems, intelligent systems. Stefan Wermter holds the Chair in Intelligent Systems and is leading the Intelligent Systems Division at the University of Sunderland, UK. His research interests are intelligent systems, neural networks, cognitive neuroscience, hybrid systems, language processing and learning robots. He has a diploma from the University of Dortmund, Germany, an MSc from the University of Massachusetts, USA, and a PhD in habilitation from the University of Hamburg, Germany, all in Computer Science. He was a Research Scientist at Berkeley, CA, before joining the University of Sunderland. Professor Wermter has written edited, or contributed to 8 books and published about 80 articles on this research area.     

Statistical methods for detecting latent periodicity patterns in biological sequences: The case of small-size samples

An original spectral-statistical approach for detecting latent periodicity in biological sequences is proposed. This approach can be applied under conditions of limited statistical sample. It allows one to avoid redundancy and instability when identifying the latent periodicity structure. The optimality of the periodicity-pattern-size estimates obtained for approximate tandem repeats on the basis of the spectral-statistical approach is demonstrated in practical examples. Maria Borisovna Chaley was born in 1963 and graduated from the Moscow Institute of Physics and Technology in 1988 (M.Sc.). She received her PhD in biophysics in 1993 and became a docent in bioinformatics in 2003. At the present time, she is a senior research fellow at the Institute of Mathematical Biology Problems of the Russian Academy of Sciences. Her research interests include bioinformatics, genetic text analysis, and molecular evolution. She is the author of over 40 research publications, including 14 journal articles. Nafisa Nailovna Nazipova was born in 1960 and graduated from the Department of Computational Mathematics and Cybernetics of Lenin Kazan State University in 1982 (MSc.). She received her PhD in physics and mathematics (mathematic modeling, numerical methods, and program complexes) in 2002. At the present time, she is the head of the bioinformatics laboratory at the Institute of Mathematical Biology Problems of the Russian Academy of Sciences. Her research interests include bioinformatics and the structural and functional organization of genetic sequences. She is the author of over 35 research publications, including 9 papers in refereed journals and 2 book chapters. Vladimir Andreyevich Kutyrkin was born in 1952 and graduated from the Department of Mechanics and Mathematics of Lomonosov Moscow State University in 1974 (MSc.). He received his PhD in physics and mathematics in 1995. At the present time, he is a docent of Bauman Moscow State Technical University. His research interests include applied mathematical statistics, computational and discrete mathematics, and bioinformatics. He is the author of over 20 research publications, including 11 journal papers.     

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.