Block cipher based on reversible cellular automata

We propose a new encryption algorithm relying on reversible cellular automata (CA). The behavior complexity of CA and their parallel nature makes them interesting candidates for cryptography. The proposed algorithm belongs to the class of symmetric key systems. Marcin Seredynski: He is a Ph.D. student at University of Luxembourg and Polish Academy of Sciences. He received his M.S. in 2004 from Faculty of Electronics and Information Technology in Warsaw University of Technology. His research interests include cryptography, cellular automata, nature inspired algorithms and network security. Currently he is working on intrusion detection algorithms for ad-hoc networks. Pascal Bouvry, Ph.D.: He earned his undergraduate degree in Economical & Social Sciences and his Master degree in Computer Science with distinction (’91) from the University of Namur, Belgium. He went on to obtain his Ph.D. degree (’94) in Computer Science with great distinction at the University of Grenoble (INPG), France. His research at the IMAG laboratory focussed on Mapping and scheduling task graphs onto Distributed Memory Parallel Computers. Next, he performed post-doctoral researches on coordination languages and multi-agent evolutionary computing at CWI in Amsterdam. He gained industrial experience as manager of the technology consultant team for FICS in the banking sector (Brussels, Belgium). Next, he worked as CEO and CTO of SDC (Ho Chi Minh city, Vietnam) in the telecom, semi-conductor and space industry. After that, He moved to Montreal Canada as VP Production of Lat45 and Development Director for MetaSolv Software in the telecom industry. He is currently serving as Professor in the group of Computer Science and Communications (CSC) of the Faculty of Sciences, Technology and Communications of Luxembourg University and he is heading the Intelligent & Adaptive Systems lab. His current research interests include: ad-hoc networks & grid-computing, evolutionary algorithms and multi-agent systems.     

Zikimi: A Case Study in Micro Kernel Design for Multimedia Applications

Due to recent rapid deployment of Internet Appliances and PostPC products, the importance of developing lightweight embedded operating system is being emphasized more. In this article, we like to present the details of design and implementation experience of low cost embedded system, Zikimi, for multimedia data processing. We use the skeleton of existing Linux operating system and develop a micro-kernel to perform a number of specific tasks efficiently and effectively. Internet Appliances and PostPC products usually have very limited amount of hardware resources to execute very specific tasks. We carefully analyze the system requirement of multimedia processing device. Weremove the unnecessary features, e.g. virtual memory, multitasking, a number of different file systems, and etc. The salient features of Zikimi micro kernel are (i) linear memory system and (ii) user level control of I/O device. The result of performance experiment shows that LMS (linear memory system) of Zikimi micro kernel achieves significant performance improvement on memory allocationagainst legacy virtual memory management system of Linux. By exploiting the computational capability of graphics processor and its local memory, we achieve 2.5 times increase in video processing speed. Supported by KOSEF through Statistical Research Center for Complex Systems at Seoul National University. Funded by Faculty Research Institute Program 2001, Sahmyook University, Korea. Sang-Yeob Lee received his B.S. and M.S degree from Hanyang University, seoul, Korea in 1995. He is currently working towards the Ph.D. degree in Devision of Electrical and Computer Engineering, Hanyang University, Seoul, Korea. Since 1998, he has been on the faculty of Information Management System at Sahmyook university, Seoul, Korea. His research interests include robot vision systems, pattern recognition, Multimedia systems. He is a member of IEEE. Youjip Won received the B.S and M.S degree in Computer Science from the Department of Computer Science, Seoul National University, Seoul, Korea in 1990 and 1992, respectively and the Ph.D. in Computer Science from the University of Minnesota, Minneapolis in 1997. After finishing his Ph.D., He worked as Server Performance Analysts at Server Architecture Lab., Intel Corp. Since 1999, he has been on the board of faculty members in Division of Electrical and Computer Engineering, Hanyang University, Seoul, Korea. His current research interests include Multimedia Systems, Internet Technology, Database and Performance Modeling and Analysis. He is a member of ACM and IEEE. Whoi-Yul Kim received his B.S. degree in Electronic Engineering from Hanyang University, Seoul, Korea in 1980. He received his M.S. from Pennsylvania State University, University Park, in 1983 and his Ph.D. from Purdue University, West Lafayette, in 1989, both in Electrical Engineering. From 1989 to 1994, he was with the Erick Jonsson School of Engineering and Computer Science at the University of Texas at Dallas. Since 1994, he has been on the faculty of Electronic Engineering at Hanyang University, Seoul, Korea. He has been involved with research development of various range sensors and their use in robot vision systems. Recently, his work has focused on content-based image retrieval system. He is a member of IEEE.     

Finding centric local outliers in categorical/numerical spaces

Outlier detection techniques are widely used in many applications such as credit-card fraud detection, monitoring criminal activities in electronic commerce, etc. These applications attempt to identify outliers as noises, exceptions, or objects around the border. The existing density-based local outlier detection assigns the degree to which an object is an outlier in a numerical space. In this paper, we propose a novel mutual-reinforcement-based local outlier detection approach. Instead of detecting local outliers as noise, we attempt to identify local outliers in the center, where they are similar to some clusters of objects on one hand, and are unique on the other. Our technique can be used for bank investment to identify a unique body, similar to many good competitors, in which to invest. We attempt to detect local outliers in categorical, ordinal as well as numerical data. In categorical data, the challenge is that there are many similar but different ways to specify relationships among the data items. Our mutual-reinforcement-based approach is stable, with similar but different user-defined relationships. Our technique can reduce the burden for users to determine the relationships among data items, and find the explanations why the outliers are found. We conducted extensive experimental studies using real datasets. Jeffrey Xu Yu received his B.E., M.E. and Ph.D. in computer science, from the University of Tsukuba, Japan, in 1985, 1987 and 1990, respectively. Jeffrey Xu Yu was a research fellow in the Institute of Information Sciences and Electronics, University of Tsukuba (Apr. 1990–Mar. 1991), and held teaching positions in the Institute of Information Sciences and Electronics, University of Tsukuba (Apr. 1991–July 1992) and in the Department of Computer Science, Australian National University (July 1992–June 2000). Currently he is an Associate Professor in the Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong. His major research interests include data mining, data stream mining/processing, XML query processing and optimization, data warehouse, on-line analytical processing, and design and implementation of database management systems. Weining Qian is currently an assistant professor of computer science at Fudan University, Shanghai, China. He received his M.S. and Ph.D. degrees in computer science from Fudan University in 2001 and 2004, respectively. He was supported by a Microsoft Research Fellowship when he was doing the research presented in this paper, and he is supported by the Shanghai Rising Star Program. His research interests include data mining for very large databases, data stream query processing and mining and peer-to-peer computing. Hongjun Lu received his B.Sc. from Tsinghua University, China, and M.Sc. and Ph.D. from the Department of Computer Science, University of Wisconsin–Madison. He worked as an engineer in the Chinese Academy of Space Technology, and a principal research scientist in the Computer Science Center of Honeywell Inc., Minnesota, USA (1985–1987), and a professor at the School of Computing of the National University of Singapore (1987–2000), and is a full professor of the Hong Kong University of Science and Technology. His research interests are in data/knowledge-base management systems with an emphasis on query processing and optimization, physical database design, and database performance. Hongjun Lu is currently a trustee of the VLDB Endowment, an associate editor of the IEEE Transactions on Knowledge and Data Engineering (TKDE), and a member of the review board of the Journal of Database Management. He served as a member of the ACM SIGMOD Advisory Board in 1998–2002. Aoying Zhou born in 1965, is currently a professor of computer science at Fudan University, Shanghai, China. He won his Bachelor degree and Master degree in Computer Science from Sichuan University in Chengdu, Sichuan, China in 1985 and 1988. respectively, and a Ph.D. degree from Fudan University in 1993. He has served as a member or chair of the program committees for many international conferences such as VLDB, ER, DASFAA, WAIM, and etc. His papers have been published in ACM SIGMOD, VLDB, ICDE and some international journals. His research interests include data mining and knowledge discovery, XML data management, web query and searching, data stream analysis and processing and peer-to-peer computing.     

Recent Progress on Selected Topics in Database Research ——-A Report by Nine Young Chinese Researchers Working in the United States

The study on database technologies, or more generally, the technologies of data and information management, is an important and active research field. Recently, many exciting results have been reported. In this fast growing field, Chinese researchers play more and more active roles. Research papers from Chinese scholars, both in China and abroad,appear in prestigious academic forums.In this paper,we, nine young Chinese researchers working in the United States, present concise surveys and report our recent progress on the selected fields that we are working on.Although the paper covers only a small number of topics and the selection of the topics is far from balanced, we hope that such an effort would attract more and more researchers,especially those in China,to enter the frontiers of database research and promote collaborations. For the obvious reason, the authors are listed alphabetically, while the sections are arranged in the order of the author list.     

Efficient Computation of k-Medians over Data Streams Under Memory Constraints

In this paper, we study the problem of efficiently computing k-medians over high-dimensional and high speed data streams. The focus of this paper is on the issue of minimizing CPU time to handle high speed data streams on top of the requirements of high accuracy and small memory. Our work is motivated by the following observation： the existing algorithms have similar approximation behaviors in practice, even though they make noticeably different worst case theoretical guarantees. The underlying reason is that in order to achieve high approximation level with the smallest possible memory, they need rather complex techniques to maintain a sketch, along time dimension, by using some existing off-line clustering algorithms. Those clustering algorithms cannot guarantee the optimal clustering result over data segments in a data stream but accumulate errors over segments, which makes most algorithms behave the same in terms of approximation level, in practice. We propose a new grid-based approach which divides the entire data set into cells （not along time dimension）. We can achieve high approximation level based on a novel concept called （1 - ε）-dominant. We further extend the method to the data stream context, by leveraging a density-based heuristic and frequent item mining techniques over data streams. We only need to apply an existing clustering once to computing k-medians, on demand, which reduces CPU time significantly. We conducted extensive experimental studies, and show that our approaches outperform other well-known approaches.     

Improving Image Retrieval Effectiveness via Multiple Queries

Conventional approaches to image retrieval are based on the assumption that relevant images are physically near the query image in some feature space. This is the basis of the cluster hypothesis. However, semantically related images are often scattered across several visual clusters. Although traditional Content-based Image Retrieval (CBIR) technologies may utilize the information contained in multiple queries (gotten in one step or through a feedback process), this is often only a reformulation of the original query. As a result most of these strategies only get the images in some neighborhood of the original query as the retrieval result. This severely restricts the system performance. Relevance feedback techniques are generally used to mitigate this problem. In this paper, we present a novel approach to relevance feedback which can return semantically related images in different visual clusters by merging the result sets of multiple queries. We also provide experimental results to demonstrate the effectiveness of our approach.Xiangyu Jin received his B.S. and M.E. in Computer Science from the Nanjing University, China, in 1999 and 2002, respectively. He has a visiting student in Microsoft Research Asia (2001) and now is a Ph.D. candidate in the Department of Computer Science at the University of Virginia. His current research interest includes multimedia information retrieval and user interface study. He had the authored or co-authored about 20 publications in these areas.James French is currently a Research Associate Professor in the Department of Computer Science at the University of Virginia. He received a B.A. in Mathematics and M.S. and Ph.D. (1982) degrees in Computer Science, all at the University of Virginia. After several years in industry, he returned to the University of Virginia in 1987 as a Senior Scientist in the Institute for Parallel Computation and joined the Department of Computer Science in 1990. His current research interests include content-based retrieval and information retrieval in widely distributed information systems. He is the editor of five books, and the author or co-author of one book and over 75 papers and book chapters. Professor French is a member of the ACM, the IEEE Computer Society, ASIST, and Sigma Xi. At the time of this work he was on a leave of absence from the University of Virginia serving as a program director at the U.S. National Science Foundation.     

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.     

Answering ad hoc aggregate queries from data streams using prefix aggregate trees

In some business applications such as trading management in financial institutions, it is required to accurately answer ad hoc aggregate queries over data streams. Materializing and incrementally maintaining a full data cube or even its compression or approximation over a data stream is often computationally prohibitive. On the other hand, although previous studies proposed approximate methods for continuous aggregate queries, they cannot provide accurate answers. In this paper, we develop a novel prefix aggregate tree (PAT) structure for online warehousing data streams and answering ad hoc aggregate queries. Often, a data stream can be partitioned into the historical segment, which is stored in a traditional data warehouse, and the transient segment, which can be stored in a PAT to answer ad hoc aggregate queries. The size of a PAT is linear in the size of the transient segment, and only one scan of the data stream is needed to create and incrementally maintain a PAT. Although the query answering using PAT costs more than the case of a fully materialized data cube, the query answering time is still kept linear in the size of the transient segment. Our extensive experimental results on both synthetic and real data sets illustrate the efficiency and the scalability of our design. Moonjung Cho is a Ph.D. candidate in the Department of Computer Science and Engineering at State University of New York at Buffalo. She obtained her Master from same university in 2003. She has industry experiences as associate researcher for 4 years. Her research interests are in the area of data mining, data warehousing and data cubing. She has received a full scholarship from Institute of Information Technology Assessment in Korea. Jian Pei received the Ph.D. degree in Computing Science from Simon Fraser University, Canada, in 2002. He is currently an Assistant Professor of Computing Science at Simon Fraser University, Canada. In 2002–2004, he was an Assistant Professor of Computer Science and Engineering at the State University of New York at Buffalo, USA. His research interests can be summarized as developing advanced data analysis techniques for emerging applications. Particularly, he is currently interested in various techniques of data mining, data warehousing, online analytical processing, and database systems, as well as their applications in bioinformatics. His current research is supported in part by Natural Sciences and Engineering Research Council of Canada (NSERC) and National Science Foundation (NSF). He has published over 70 papers in refereed journals, conferences, and workshops, has served in the program committees of over 60 international conferences and workshops, and has been a reviewer for some leading academic journals. He is a member of the ACM, the ACM SIGMOD, and the ACM SIGKDD. 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. Ke 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. Ke Wang has published in database, information retrieval, and data mining conferences, including SIGMOD, SIGIR, PODS, VLDB, ICDE, EDBT, SIGKDD, SDM and ICDM. He is an associate editor of the IEEE TKDE journal and has served program committees for international conferences including DASFAA, ICDE, ICDM, PAKDD, PKDD, SIGKDD and VLDB.     

Extracting semantic concepts from images: a decisive feature pattern mining approach

One major challenge in the content-based image retrieval (CBIR) and computer vision research is to bridge the so-called “semantic gap” between low-level visual features and high-level semantic concepts, that is, extracting semantic concepts from a large database of images effectively. In this paper, we tackle the problem by mining the decisive feature patterns (DFPs). Intuitively, a decisive feature pattern is a combination of low-level feature values that are unique and significant for describing a semantic concept. Interesting algorithms are developed to mine the decisive feature patterns and construct a rule base to automatically recognize semantic concepts in images. A systematic performance study on large image databases containing many semantic concepts shows that our method is more effective than some previously proposed methods. Importantly, our method can be generally applied to any domain of semantic concepts and low-level features. Wei Wang received his Ph.D. degree in Computing Science and Engineering from the State University of New York (SUNY) at Buffalo in 2004, under Dr. Aidong Zhang's supervision. He received the B.Eng. in Electrical Engineering from Xi'an Jiaotong University, China in 1995 and the M.Eng. in Computer Engineering from National University of Singapore in 2000, respectively. He joined Motorola Inc. in 2004, where he is currently a senior research engineer in Multimedia Research Lab, Motorola Applications Research Center. His research interests can be summarized as developing novel techniques for multimedia data analysis applications. He is particularly interested in multimedia information retrieval, multimedia mining and association, multimedia database systems, multimedia processing and pattern recognition. He has published 15 research papers in refereed journals, conferences, and workshops, has served in the organization committees and the program committees of IADIS International Conference e-Society 2005 and 2006, and has been a reviewer for some leading academic journals and conferences. In 2005, his research prototype of “seamless content consumption” was awarded the “most innovative research concept of the year” from the Motorola Applications Research Center. Dr. Aidong Zhang received her Ph.D. degree in computer science from Purdue University, West Lafayette, Indiana, in 1994. She was an assistant professor from 1994 to 1999, an associate professor from 1999 to 2002, and has been a professor since 2002 in the Department of Computer Science and Engineering at the State University of New York at Buffalo. Her research interests include bioinformatics, data mining, multimedia systems, content-based image retrieval, and database systems. She has authored over 150 research publications in these areas. Dr. Zhang's research has been funded by NSF, NIH, NIMA, and Xerox. Dr. Zhang serves on the editorial boards of International Journal of Bioinformatics Research and Applications (IJBRA), ACMMultimedia Systems, the International Journal of Multimedia Tools and Applications, and International Journal of Distributed and Parallel Databases. She was the editor for ACM SIGMOD DiSC (Digital Symposium Collection) from 2001 to 2003. She was co-chair of the technical program committee for ACM Multimedia 2001. She has also served on various conference program committees. Dr. Zhang is a recipient of the National Science Foundation CAREER Award and SUNY Chancellor's Research Recognition Award.     

Algebraic retrieval of fragmentarily indexed video

When dealing with long video data, the task of identifying and indexing all meaningful subintervals that become answers to some queries is infeasible. It is infeasible not only when done by hand but even when done by using latest automatic video indexing techniques. Whether manually or automatically, it is only fragmentary video intervals that we can identify in advance of any database usage. Our goal is to develop a framework for retrieving meaningful intervals from such fragmentarily indexed video data. We propose a set of algebraic operations that includes ourglue join operations, with which we can dynamically synthesize all the intervals that are conceivably relevant to a given query. In most cases, since these operations also produce irrelevant intervals, we also define variousselection operations that are useful in excluding them from the answer set. We also show the algebraic properties possessed by those operations, which establish the basis of an algebraic query optimization. Katsumi Tanaka, D. Eng.: He received his B.E., M.E., and D.Eng. degrees in information science from Kyoto University, in 1974, 1976, and 1981, respectively. Since 1994, he is a professor of the Department of Computer and Systems Engineering and since 1997, he is a professor of the Division of Information and Media Sciences, Graduate School of Science and Technology, Kobe University. His research interests include object-oriented, multimedia and historical databases abd multimedia information systems. He is a member of the ACM, IEEE Computer Society and the Information Processing Society of Japan. Keishi Tajima, D.Sci.: He received his B.S, M.S., and D.S. from the department of information science of University of Tokyo in 1991, 1993, and 1996 respectively. Since 1996, he is a Research Associate in the Department of Computer and Systems Engineering at Kobe University. His research interests include data models for non-traditional database systems and their query languages. He is a member of ACM, ACM SIGMOD, Information Processing Society of Japan (IPSJ), and Japan Society for Software Science and Technology (JSSST). Takashi Sogo, M.Eng.: He received B.E. and M.E. from the Department of Computer and Systems Engineering, Kobe University in 1998 and 2000, respectively. Currently, he is with USAC Systems Co. His research interests include video database systems. Sujeet Pradhan, D.Eng.: He received his BE in Mechanical Engineering from the University of Rajasthan, India in 1988, MS in Instrumentation Engineering in 1995 and Ph.D. in Intelligence Science in 1999 from Kobe University, Japan. Since 1999 May, he is a lecturer of the Department of Computer Science and Mathematics at Kurashiki University of Science and the Arts, Japan. A JSPS (Japan Society for the Promotion of Science) Research Fellow during the period between 1997 and 1999, his research interests include video databases, multimedia authoring, prototypebased languages and semi-structured databases. Dr. Pradhan is a member of Information Processing Society of Japan.     

Behavior of the Laplacian of Gaussian Extrema

This paper analyses the behavior in scale space of linear junction models (L, Y and X models), nonlinear junction models, and linear junction multi-models. The variation of the grey level is considered to be constant, linear or nonlinear in the case of linear models and constant for the other models. We are mainly interested in the extrema points provided by the Laplacian of the Gaussian function. Moreover, we show that for infinite models the Laplacian of the Gaussian at the corner point is not always equal to zero.Salvatore Tabbone received his Ph.D. in computer science from the Institut National Polytechnique de Lorraine (France) in 1994. He is currently an assistant professor at the University of Nancy2 (France) and a member of the QGAR research project on graphics recognition at the LORIA-INRIA research center. His research interests include computer vision, pattern recognition, content-based image retrieval, and document analysis and recognition.Laurent Alonso was a student of ENS Ulm from 1987 to 1991, he received the Ph.D. degree in Computer Science from the University of Paris XI, Orsay, France in 1992. From 1991 to 1995 he served as lecturer in the University of Nancy I (France). Actually, he is full researcher in INRIA (France). His research interests include realistic rendering, geometric algorithms and combinatorics.Djemel Ziou received the BEng Degree in Computer Science from the University of Annaba (Algeria) in 1984, and Ph.D. degree in Computer Science from the Institut National Polytechnique de Lorraine (INPL), France in 1991. From 1987 to 1993 he served as lecturer in several universities in France. During the same period, he was a researcher in the Centre de Recherche en Informatique de Nancy (CRIN) and the Institut National de Recherche en Informatique et Automatique (INRIA) in France. Presently, he is full Professor at the department of computer science at the University of Sherbrooke in Canada. He has served on numerous conference committees as member or chair. He heads the laboratory MOIVRE and the consortium CoRIMedia which he founded. His research interests include image processing, information retrieval, computer vision and pattern recognition.     

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.     

Adaptation in Multimedia Systems

Multimedia systems can profit a lot from personalization. Such a personalization is essential to give users the feeling that the system is easily accessible especially if it is done automatically. The way this adaptive personalization works is very dependent on the adaptation model that is chosen.We introduce a generic two-dimensional classification framework for user modeling systems. This enables us to clarify existing as well as new applications in the area of user modeling. In order to illustrate our framework we evaluate push and pull based user modeling in user modeling systems.Paul de Vrieze received his Masters degree in Information Science in 2002 from the University Of Tilburg, The Netherlands. He is currently junior researcher at the University of Nijmegen. His main research interests include adaptive systems and user modelling.Patrick van Bommel received his Masters degree in Computer Science in 1990, and the degree of Ph.D in Mathematics and Computer Science, from the University of Nijmegen, the Netherlands in 1995. He is currently assistant professor at the University of Nijmegen. His main research interests include information modelling and information retrieval.Prof.Dr.Ir. Th.P. van der Weide received his masters degree from the Technical University Eindhoven, the Netherlands in 1975, and the degree of Ph.D in Mathematics and Physics from the University of Leiden, the Netherlands in 1980. He is currently professor at the University of Nijmegen, the Netherlands. His main research interests include information systems, information retrieval, hypertext and knowledge based systems.     

A Framework for the Generation of Adaptive Courses Based on Semantic Metadata

This approach proposes the creation and management of adaptive learning systems by combining component technology, semantic metadata, and adaptation rules. A component model allows interaction among components that share consistent assumptions about what each provides and each requires of the other. It allows indexing, using, reusing, and coupling of components in different contexts powering adaptation. Our claim is that semantic metadata are required to allow a real reusing and assembling of educational component. Finally, a rule language is used to define strategies to rewrite user query and user model. The former allows searching components developing concepts not appearing in the user query but related with user goals, whereas the last allow inferring user knowledge that is not explicit in user model.John Freddy Duitama received his M.Sc. degree in system engineering from the University of Antioquia -Colombia (South America). He is currently a doctoral candidate in the GET – Institut National des Télécommunications, Evry France. This work is sponsored by the University of Antioquia, where he is assistant professor.His research interest includes semantic web and web-based learning systems, educational metadata and learning objects.Bruno Defude received his Ph.D. in Computer Science from the University of Grenoble (I.N.P.G) in 1986. He is currently Professor in the Department of Computer Science at the GET - Institut National des Télécommunications, Evry France where he leads the SIMBAD project (Semantic Interoperability for MoBile and ADaptive applications).His major field of research interest is databases and semantic web, specifically personalized data access, adaptive systems, metadata, interoperability and semantic Peer-to-peer systems with elearning as a privileged application area.He is a member of ACM SIGMOD.Amel Bouzeghoub received a degree of Ph.D. in Computer Sciences at Pierre et Marie Curie University, France.In 2000, she joined the Computer Sciences Department of GET-INT (Institut National des Telecommunications) at Evry (France) as an associate professor.Her research interests include topics related to Web-based Learning Systems, Semantic Metadata for learning resources, Adaptive Learning Systems and Intelligent Tutoring Systems.Claire Lecocq received an Engineer Degree and a Ph.D. in Computer Sciences respectively in 1994 and 1999. In 1997, she joined the Computer Sciences Department at GET-INT (Institut National des Télécommunications) of Evry, France, as an associate professor. Her first research interests included spatial databases and visual query languages. She is now working on adaptive learning systems, particularly on semantic metadata and user models.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

Applying language modeling to session identification from database trace logs

A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement.In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-labeled training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, learning from labeled data, learning from semi-labeled data and learning from unlabeled data, are introduced to learn language models from different types of training data. Finally, we report experimental results that show the effectiveness of the language model based method for identifying sessions from the trace logs of an OLTP database application and the TPC-C Benchmark. Xiangji Huang joined York University as an Assistant Professor in July 2003 and then became a tenured Associate Professor in May 2006. Previously, he was a Post Doctoral Fellow at the School of Computer Science, University of Waterloo, Canada. He did his Ph.D. in Information Science at City University in London, England, with Professor Stephen E. Robertson. Before he went into his Ph.D. program, he worked as a lecturer for 4 years at Wuhan University. He also worked in the financial industry in Canada doing E-business, where he was awarded a CIO Achievement Award, for three and half years. He has published more than 50 refereed papers in journals, book chapter and conference proceedings. His Master (M.Eng.) and Bachelor (B.Eng.) degrees were in Computer Organization & Architecture and Computer Engineering, respectively. His research interests include information retrieval, data mining, natural language processing, bioinformatics and computational linguistics. Qingsong Yao is a Ph.D. student in the Department of Computer Science and Engineering at York University, Toronto, Canada. His research interests include database management systems and query optimization, data mining, information retrieval, natural language processing and computational linguistics. He earned his Master's degree in Computer Science from Institute of Software, Chinese Academy of Science in 1999 and Bachelor's degree in Computer Science from Tsinghua University. Aijun An is an associate professor in the Department of Computer Science and Engineering at York University, Toronto, Canada. She received her Bachelor's and Master's degrees in Computer Science from Xidian University in China. She received her PhD degree in Computer Science from the University of Regina in Canada in 1997. She worked at the University of Waterloo as a postdoctoral fellow from 1997 to 1999 and as a research assistant professor from 1999 to 2001. She joined York University in 2001. She has published more than 60 papers in refereed journals and conference proceedings. Her research interests include data mining, machine learning, and information retrieval.