XCQ: A queriable XML compression system

XML has already become the de facto standard for specifying and exchanging data on the Web. However, XML is by nature verbose and thus XML documents are usually large in size, a factor that hinders its practical usage, since it substantially increases the costs of storing, processing, and exchanging data. In order to tackle this problem, many XML-specific compression systems, such as XMill, XGrind, XMLPPM, and Millau, have recently been proposed. However, these systems usually suffer from the following two inadequacies: They either sacrifice performance in terms of compression ratio and execution time in order to support a limited range of queries, or perform full decompression prior to processing queries over compressed documents.In this paper, we address the above problems by exploiting the information provided by a Document Type Definition (DTD) associated with an XML document. We show that a DTD is able to facilitate better compression as well as generate more usable compressed data to support querying. We present the architecture of the XCQ, which is a compression and querying tool for handling XML data. XCQ is based on a novel technique we have developed called DTD Tree and SAX Event Stream Parsing (DSP). The documents compressed by XCQ are stored in Partitioned Path-Based Grouping (PPG) data streams, which are equipped with a Block Statistics Signature (BSS) indexing scheme. The indexed PPG data streams support the processing of XML queries that involve selection and aggregation, without the need for full decompression. In order to study the compression performance of XCQ, we carry out comprehensive experiments over a set of XML benchmark datasets. Wilfred Ng obtained his M.Sc.(Distinction) and Ph.D. degrees from the University of London. His research interests are in the areas of databases and information Systems, which include XML data, database query languages, web data management, and data mining. He is now an assistant professor in the Department of Computer Science, the Hong Kong University of Science and Technology (HKUST). Further Information can be found at the following URL: . Wai-Yeung Lam obtained his M.Phil. degree from the Hong Kong University of Science and Technology (HKUST) in 2003. His research thesis was based on the project “XCQ: A Framework for Querying Compressed XML Data.” He is currently working in industry. Peter Wood received his Ph.D. in Computer Science from the University of Toronto in 1989. He has previously studied at the University of Cape Town, South Africa, obtaining a B.Sc. degree in 1977 and an M.Sc. degree in Computer Science in 1982. Currently he is a senior lecturer at Birkbeck and a member of the Information Management and Web Technologies research group. His research interests include database and XML query languages, query optimisation, active and deductive rule languages, and graph algorithms. Mark Levene received his Ph.D. in Computer Science in 1990 from Birkbeck College, University of London, having previously been awarded a B.Sc. in Computer Science from Auckland University, New Zealand in 1982. He is currently professor of Computer Science at Birkbeck College, where he is a member of the Information Management and Web Technologies research group. His main research interests are Web search and navigation, Web data mining and stochastic models for the evolution of the Web. He has published extensively in the areas of database theory and web technologies, and has recently published a book called ‘An Introduction to Search Engines and Web Navigation’.     

Seeing things: Inventive reasoning with geometric analogies and topographic maps

This paper examines two seemingly unrelated qualitative spatial reasoning domains; geometric proportional analogies and topographic (land-cover) maps. We present a Structure Matching algorithm that combines Gentner’s structuremapping theory with an attributematching process. We use structure matching to solve geometric analogy problems that involve manipulating attribute information, such as colors and patterns. Structure matching is also used to creatively interpret topographic (land-cover) maps, adding a wealth of semantic knowledge and providing a far richer interpretation of the raw data. We return to the geometric proportional analogies, identify alternate attribute matching processes that are required to solve different categories of problems. Finally, we assess some implications for computationally creative and inventive models. Diarmuid P. O’Donoghue, Ph.D.: He received his B.Sc. and M.Sc. from University College Cork in 1988 and 1990, and his Ph.D. from University College Dublin. He has been a lecturer at the Department of Computer Science NUI Maynooth since 1996 and is also an associate of the National Centre for Geocomputation. His interests are in artificial intelligence, analogical reasoning, topology, and qualitative spatial reasoning. Amy Bohan, B.Sc, M.Sc.: She received her B.Sc. from the National University of Ireland, Maynooth in 2000. She received her M.Sc. in 2003 from University College Dublin where she also recently completed her Ph.D. She is a member of the Cognitive Science society. Her interests are in cognitive science, analogical argumentation, geometric proportional analogies and computational linguistics. Prof. Mark T. Keane: He is Chair of Computer Science and Associate Dean of Science at University College Dublin. He is also Director of ICT, at Science Foundation Ireland. Prof. Keane has made significant contributions in the areas of analogy, case-based reasoning and creativity. He has published over 100 publications, including 16 books, that are cited widely. He is co-author of a Cognitive Science textbook, written with Mike Eysenck (University of London) that has been translated into Portuguese, Hungarian, Italian and Chinese and is now entering its fifth edition. Prof. Keane is a fellow of ECCAI (European Co-ordinating Committee on Artificial Intelligence) and received the Special Award for Merit from the Psychology Society of Ireland, for his work on human creativity.     

A typology of course of motion in simulated environments based on Bézier curve analysis

This paper proposes a novel method of analysing trajectories followed by people while they perform navigational tasks. The results indicate that modelling trajectories with Bézier curves provides a basis for the diagnosis of navigational patterns. The method offers five indicators: goodness of fit, average curvature, number of inflexion points, lengths of straight line segments, and area covered. Study results, obtained in a virtual environment show that these indicators carry important information about user performance, specifically spatial knowledge acquisition. Corina Sas is a Lecturer in the field of human–computer interaction in the Computing Department at Lancaster University. She holds bachelor degrees in Computer Science and Psychology and an M.A. in Industrial Psychology from Romania. She received her Ph.D. degree in Computer Science from University College Dublin in 2004. Her research interests include user modelling, adaptive systems, data mining, spatial cognition, user studies and individual differences. She has published in various journals and international conferences in these areas. Nikita Schmidt is a Postdoctoral Research Fellow at University College Dublin (UCD). He received his Ph.D. degree from UCD in 2004 and M.Sc. from St-Petersburg State University, Russia in 1994. His research interests include pervasive, ubiquitous and location-aware computing, embedded systems, hardware-close software development and tree-structured data. His work experience is a mix of industry and academia.     

STAMP: A Model for Generating Adaptable Multimedia Presentations

The STAMP model addresses the dynamic generation of multimedia presentations in the domain of Multimedia Web-based Information Systems. STAMP allows the presentation of multimedia data obtained from XML compatible data sources by means of query. Assuming that the size and the nature of the elements of information provided by a data source is not known a priori, STAMP proposes templates which describe the spatial, temporal, navigational structuration of multimedia presentations whose content varies. The instantiation of a template is done with respect to the set of spatial and temporal constraints associated with the delivery context. A set of adaptations preserving the initial intention of the presentation is proposed.Ioan Marius Bilasco is a Ph.D. student at the University Joseph Fourier in Grenoble, France, since 2003. He received his BS degree in Computer Science form the University Babes Bolyai in Cluj-Napoca, Romania and his MS degree in Computer Science from the University Joseph Fourier in Grenoble, France. He joined the LSR-IMAG Laboratory in Grenoble in 2001. His research interests include adaptability in Web-based Information Systems, 3D multimedia data modelling and mobile communications.Jérôme Gensel is an Assistant Professor at the University Pierre Mendès France in Grenoble, France, since 1996. He received his Ph.D. in 1995 from the University of Grenoble for his work on Constraint Programming and Knowledge Representation in the Sherpa project at the French National Institute of Computer Sciences and Automatics (INRIA). He joined the LSR-IMAG Laboratory in Grenoble in 2001. His research interests include adaptability and cooperation in Web-based Information Systems, multimedia data (especially video) modeling, semi-structured and object-based knowledge representation and constraint programming.Marlène Villanova-Oliver is an Assistant Professor at the University Pierre Mendès France in Grenoble, France, since 2003. In 1999, she received her MS degree in Computer Science from the University Joseph Fourier of Grenoble and the European Diploma of 3rd cycle in Management and Technology of Information Systems (MATIS). She received her Ph.D. in 2002 from the National Polytechnic Institute of Grenoble (INPG). She is a member of the LSR-IMAG Laboratory in Grenoble since 1998. Her research interests include adaptability in Web-based Information Systems, user modeling, adaptable Web Services.     

Mining the meaningful term conjunctions from materialised faceted taxonomies: algorithms and complexity

A materialised faceted taxonomy is an information source where the objects of interest are indexed according to a faceted taxonomy. This paper shows how from a materialised faceted taxonomy, we can mine an expression of the Compound Term Composition Algebra that specifies exactly those compound terms (conjunctions of terms) that have non-empty interpretation. The mined expressions can be used for encoding in a very compact form (and subsequently reusing), the domain knowledge that is stored in existing materialised faceted taxonomies. A distinctive characteristic of this mining task is that the focus is given on minimising the storage space requirements of the mined set of compound terms. This paper formulates the problem of expression mining, gives several algorithms for expression mining, analyses their computational complexity, provides techniques for optimisation, and discusses several novel applications that now become possible. Yannis Tzitzikas is currently Adjunct Professor in the Computer Science Department at University of Crete (Greece) and Visiting Researcher in Information Systems Lab at FORTH-ICS (Greece). Before joining University of Crete and FORTH-ICS, he was a postdoctoral fellow at the University of Namur (Belgium) and ERCIM postdoctoral fellow at ISTI-CNR (Pisa, Italy) and at VTT Technical Research Centre of Finland. He conducted his undergraduate and graduate studies (M.Sc., Ph.D.) in the Computer Science Department at University of Crete. His research interests fall in the intersection of the following areas: knowledge representation and reasoning, information indexing and retrieval, conceptual modeling, and collaborative distributed applications. His current research revolves around faceted metadata and semantics (theory and applications), the P2P paradigm (focusing on query evaluation algorithms and automatic schema integration techniques) and flexible interaction schemes for information bases. The results of his research are published in more than 30 papers in refereed international journals and conferences. Anastasia Analyti earned a B.S. degree in Mathematics from University of Athens, Greece, and M.S. and Ph.D. degrees in Computer Science from Michigan State University, USA. She worked as a visiting professor at the Department of Computer Science, University of Crete, and at the Department of Electronic and Computer Engineering, Technical University of Crete. Since 1995, she has been a researcher at the Information Systems Laboratory of the Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH-ICS). Her current interests include the semantic Web, conceptual modelling, faceted metadata and semantics, rules for the semantic Web, biomedical ontologies, contextual organisation of information, contextual web-ontology languages, information integration and retrieval systems for the Web. She has published over 30 papers in refereed journals and conferences.     

A logic-based integration of active and deductive databases

A logic-based approach to the specification of active database functionality is presented which not only endows active databases with a well-defined and well-understood formal semantics, but also tightly integrates them with deductive databases. The problem of endowing deductive databases with rule-based active behaviour has been addressed in different ways. Typical approaches include accounting for active behaviour by extending the operational semantics of deductive databases, or, conversely, accounting for deductive capabilities by constraining the operational semantics of active databases. The main contribution of the paper is an alternative approach in which a class of active databases is defined whose operational semantics is naturally integrated with the operational semantics of deductive databases without either of them strictly subsuming the other. The approach is demonstrated via the formalization of the syntax and semantics of an active-rule language that can be smoothly incorporated into existing deductive databases, due to the fact that the standard formalization of deductive databases is reused, rather than altered or extended. One distinctive feature of the paper is its use of ahistory, as defined in the Kowalski-Sergot event-calculus, to define event occurrences, database states and actions on these. This has proved to be a suitable foundation for a comprehensive logical account of the concept set underpinning active databases. The paper thus contributes a logical perspective to the ongoing task of developing a formal theory of active databases. Alvaro Adolfo Antunes Fernandes, Ph.D.: He received a B.Sc. in Economics (Rio de Janeiro, 1984), an M.Sc. in Knowledge-Based Systems (Edinburgh, 1990) and a Ph.D. in Computer Science (Heriot-Watt, 1995). He worked as a Research Associate at Heriot-Watt University from December 1990 until December 1995. In January 1996 he joined the Department of Mathematical and Computing Sciences at Goldsmiths College, University of London, as a Lecturer. His current research interests include advanced data- and knowledge-base technology, logic programming, and software engineering. M. Howard Williams, Ph.D., D.Sc.: He obtained his Ph.D. in ionospheric physics and recently a D.Sc. in Computer Science. He was appointed as the first lecturer in Computer Science at Rhodes University in 1970. During the following decade he rose to Professor of Computer Science and in 1980 was appointed as Professor of Computer Science at Heriot-Watt University. From 1980 to 1988 he served as Head of Department and then as director of research until 1992. He is now head of the Database Research Group at Heriot-Watt University. His current research interests include active databases, deductive objectoriented databases, spatial databases, parallel databases and telemedicine. Norman W. Paton, Ph.D.: He received a B.Sc. in Computing Science from the University of Aberdeen in 1986. From 1986 to 1989 he worked as a Research Assistant at the University of Aberdeen, receiving a Ph. D. in 1989. From 1989 to 1995 he was a Lecturer in Computer Science at Heriot-Watt University. Since July 1995, he has been a Senior Lecturer in Department of Computer Science at the University of Manchester. His current research interests include active databases, deductive object-oriented databases, spatial databases and database interfaces.     

Inference of abduction theories for handling incompleteness in first-order learning

In real-life domains, learning systems often have to deal with various kinds of imperfections in data such as noise, incompleteness and inexactness. This problem seriously affects the knowledge discovery process, specifically in the case of traditional Machine Learning approaches that exploit simple or constrained knowledge representations and are based on single inference mechanisms. Indeed, this limits their capability of discovering fundamental knowledge in those situations. In order to broaden the investigation and the applicability of machine learning schemes in such particular situations, it is necessary to move on to more expressive representations which require more complex inference mechanisms. However, the applicability of such new and complex inference mechanisms, such as abductive reasoning, strongly relies on a deep background knowledge about the specific application domain. This work aims at automatically discovering the meta-knowledge needed to abduction inference strategy to complete the incoming information in order to handle cases of missing knowledge. Floriana Esposito received the Laurea degree in electronic Physics from the University of Bari, Italy, in 1970. Since 1994 is Full Professor of Computer Science at the University of Bari and Dean of the Faculty of Computer Science from 1997 to 2002. She founded and chairs the Laboratory for Knowledge Acquisition and Machine Learning of the Department of Computer Science. Her research activity started in the field of numerical models and statistical pattern recognition. Then her interests moved to the field of Artificial Intelligence and Machine Learning. The current research concerns the logical and algebraic foundations of numerical and symbolic methods in machine learning with the aim of the integration, the computational models of incremental and multistrategy learning, the revision of logical theories, the knowledge discovery in data bases. Application include document classification and understanding, content based document retrieval, map interpretation and Semantic Web. She is author of more than 270 scientific papers and is in the scientific committees of many international scientific Conferences in the field of Artificial Intelligence and Machine Learning. She co-chaired ICML96, MSL98, ECML-PKDD 2003, IEA-AIE 2005, ISMIS 2006. Stefano Ferilli was born in 1972. After receiving his Laurea degree in Information Science in 1996, he got a Ph.D. in Computer Science at the University of Bari in 2001. Since 2002 he is an Assistant Professor at the Department of Computer Science of the University of Bari. His research interests are centered on Logic and Algebraic Foundations of Machine Learning, Inductive Logic Programming, Theory Revision, Multi-Strategy Learning, Knowledge Representation, Electronic Document Processing and Digital Libraries. He participated in various National and European (ESPRIT and IST) projects concerning these topics, and is a (co-)author of more than 80 papers published on National and International journals, books and conferences/workshops proceedings. Teresa M.A. Basile got the Laurea degree in Computer Science at the University of Bari, Italy (2001). In March 2005 she discussed a Ph.D. thesis in Computer Science at the University of Bari titled “A Multistrategy Framework for First-Order Rules Learning.” Since April 2005, she is a research at the Computer Science Department of the University of Bari working on methods and techniques of machine learning for the Semantic Web. Her research interests concern the investigation of symbolic machine learning techniques, in particular of the cooperation of different inferences strategies in an incremental learning framework, and their application to document classification and understanding based on their semantic. She is author of about 40 papers published on National and International journals and conferences/workshops proceedings and was/is involved in various National and European projects. Nicola Di Mauro got the Laurea degree in Computer Science at the University of Bari, Italy. From 2001 he went on making research on machine learning in the Knowledge Acquisition and Machine Learning Laboratory (LACAM) at the Department of Computer Science, University of Bari. In March 2005 he discussed a Ph.D. thesis in Computer Science at the University of Bari titled “First Order Incremental Theory Refinement” which faces the problem of Incremental Learning in ILP. Since January 2005, he is an assistant professor at the Department of Computer Science, University of Bari. His research activities concern Inductive Logic Programming (ILP), Theory Revision and Incremental Learning, Multistrategy Learning, with application to Automatic Document Processing. On such topics HE is author of about 40 scientific papers accepted for presentation and publication on international and national journals and conference proceedings. He took part to the European projects 6th FP IP-507173 VIKEF (Virtual Information and Knowledge Environment Framework) and IST-1999-20882 COLLATE (Collaboratory for Annotation, Indexing and Retrieval of Digitized Historical Archive Materials), and to various national projects co-funded by the Italian Ministry for the University and Scientific Research.     

Data extraction from the web based on pre-defined schema

With the development of the Internet,the World Web has become an invaluable information source for most organizations,However,most documents available from the Web are in HTML form which is originally designed for document formatting with little consideration of its contents.Effectively extracting data from such documents remains a non-trivial task.In this paper,we present a schema-guided approach to extracting data from HTML pages .Under the approach,the user defines a schema specifying what to be extracted and provides sample mappings between the schema and th HTML page.The system will induce the mapping rules and generate a wrapper that takes the HTML page as input and produces the required datas in the form of XML conforming to the use-defined schema .A prototype system implementing the approach has been developed .The preliminary experiments indicate that the proposed semi-automatic approach is not only easy to use but also able to produce a wrapper that extracts required data from inputted pages with high accuracy.     

Handling non-canonical software requirements based on Annotated Predicate Calculus

Eliciting requirements for a proposed system inevitably involves the problem of handling undesirable information about customer's needs, including inconsistency, vagueness, redundancy, or incompleteness. We term the requirements statements involved in the undesirable information non-canonical software requirements. In this paper, we propose an approach to handling non-canonical software requirements based on Annotated Predicate Calculus (APC). Informally, by defining a special belief lattice appropriate for representing the stakeholder's belief in requirements statements, we construct a new form of APC to formalize requirements specifications. We then show how the APC can be employed to characterize non-canonical requirements. Finally, we show how the approach can be used to handle non-canonical requirements through a case study. Kedian Mu received B.Sc. degree in applied mathematics from Beijing Institute of Technology, Beijing, China, in 1997, M.Sc. degree in probability and mathematical statistics from Beijing Institute of Technology, Beijing, China, in 2000, and Ph.D. in applied mathematics from Peking University, Beijing, China, in 2003. From 2003 to 2005, he was a postdoctoral researcher at Institute of Computing Technology, Chinese Academy of Sciences, China. He is currently an assistant professor at School of Mathematical Sciences, Peking University, Beijing, China. His research interests include uncertain reasoning in artificial intelligence, knowledge engineering and science, and requirements engineering. Zhi Jin was awarded B.Sc. in computer science from Zhejiang University, Hangzhou, China, in 1984, and studied for her M.Sc. in computer science (expert system) and her Ph.D. in computer science (artificial intelligence) at National Defence University of Technology, Changsha, China. She was awarded Ph.D. in 1992. She is a senior member of China Computer Federation. She is currently a professor at Academy of Mathematics and System Sciences, Chinese Academy of Science. Her research interests include knowledge-based systems, artificial intelligence, requirements engineering, ontology engineering, etc. Her current research focuses on ontology-based requirements elicitation and analysis. She has got about 60 papers published, including co-authoring one book. Ruqian Lu is a professor of computer science of the Institute of Mathematics, Chinese Academy of Sciences. His research interests include artificial intelligence, knowledge engineering and knowledge based software engineering. He designed the “Tian Ma” software systems that have been widely applied in more than 20 fields, including the national defense and the economy. He has won two first class awards from Chinese Academy of Sciences and a National second class prize from the Ministry of Science and Technology. He has also won the sixth Hua Lookeng Prize for Mathematics. Yan Peng received B.Sc. degree in software from Jilin University, Changchun, China, in 1992. From June 2002 to December 2005, he studied for his M.E. in software engineering at College of Software Engineering, Graduate School of Chinese Academy of Sciences, Beijing, China. He was awarded M.E degree in 2006. He is currently responsible for CRM (customer relationship management) and BI (business intelligence) project in the BONG. His research interests include customer relationship management, business intelligence, data ming, software engineering and requirements engineering.     

TEG—a hybrid approach to information extraction

This paper describes a hybrid statistical and knowledge-based information extraction model, able to extract entities and relations at the sentence level. The model attempts to retain and improve the high accuracy levels of knowledge-based systems while drastically reducing the amount of manual labour by relying on statistics drawn from a training corpus. The implementation of the model, called TEG (trainable extraction grammar), can be adapted to any IE domain by writing a suitable set of rules in a SCFG (stochastic context-free grammar)-based extraction language and training them using an annotated corpus. The system does not contain any purely linguistic components, such as PoS tagger or shallow parser, but allows to using external linguistic components if necessary. We demonstrate the performance of the system on several named entity extraction and relation extraction tasks. The experiments show that our hybrid approach outperforms both purely statistical and purely knowledge-based systems, while requiring orders of magnitude less manual rule writing and smaller amounts of training data. We also demonstrate the robustness of our system under conditions of poor training-data quality. Ronen Feldman is a senior lecturer at the Mathematics and Computer Science Department of Bar-Ilan University in Israel, and the Director of the Data Mining Laboratory. He received his B.Sc. in Math, Physics and Computer Science from the Hebrew University, M.Sc. in Computer Science from Bar-Ilan University, and his Ph.D. in Computer Science from Cornell University in NY. He was an Adjunct Professor at NYU Stern Business School. He is the founder of ClearForest Corporation, a Boston based company specializing in development of text mining tools and applications. He has given more than 30 tutorials on next mining and information extraction and authored numerous papers on these topics. He is currently finishing his book “The Text Mining Handbook” to the published by Cambridge University Press. Benjamin Rosenfeld is a research scientist at ClearForest Corporation. He received his B.Sc. in Mathematics and Computer Science from Bar-Ilan University. He is the co-inventor of the DIAL information extraction language. Moshe Fresko is finalizing his Ph.D. in Computer Science Department at Bar-Ilan University in Israel. He received his B.Sc. in Computer Engineering from Bogazici University, Istanbul/Turkey on 1991, and M.Sc. on 1994. He is also an adjunct lecturer at the Computer Science Department of Bar-Ilan University and functions as the Information-Extraction Group Leader in the Data Mining Laboratory.     

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

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

Evolutionary fusion of a multi-classifier system for efficient face recognition

In this paper an evolutionary classifier fusion method inspired by biological evolution is presented to optimize the performance of a face recognition system. Initially, different illumination environments are modeled as multiple contexts using unsupervised learning and then the optimized classifier ensemble is searched for each context using a Genetic Algorithm (GA). For each context, multiple optimized classifiers are searched; each of which are referred to as a context based classifier. An evolutionary framework comprised of a combination of these classifiers is then applied to optimize face recognition as a whole. Evolutionary classifier fusion is compared with the simple adaptive system. Experiments are carried out using the Inha database and FERET database. Experimental results show that the proposed evolutionary classifier fusion method gives superior performance over other methods without using evolutionary fusion. Recommended by Guest Editor Daniel Howard. This work was supported by INHA UNIVERSITY Research Grant. Zhan Yu received the B.E. degree in Software Engineering from Xiamen University, China, in 2008. He is currently a master student in Intelligent Technology Lab, Computer and Information Department, Inha University, Korea. He has research interests in image processing, pattern recognition, computer vision, machine learning and statistical inference and computating. Mi Young Nam received the B.Sc. and M.Sc. degrees in Computer Science from the University of Silla Busan, Korea in 1995 and 2001 respectively and the Ph.D. degree in Computer Science & Engineering from the University of Inha, Korea in 2006. Currently, She is Post-Doctor course in Intelligent Technology Laboratory, Inha University, Korea. She’s research interest includes biometrics, pattern recognition, computer vision, image processing. Suman Sedai received the M.S. degree in Software Engineering from Inha University, China, in 2008. He is currently a Doctoral course in Western Australia University, Australia. He has research interests in image processing, pattern recognition, computer vision, machine learning. Phill Kyu Rhee received the B.S. degree in Electrical Engineering from the Seoul University, Seoul, Korea, the M.S. degree in Computer Science from the East Texas State University, Commerce, TX, and the Ph.D. degree in Computer Science from the University of Louisiana, Lafayette, LA, in 1982, 1986, and 1990 respectively. During 1982–1985 he was working in the System Engineering Research Institute, Seoul, Korea as a research scientist. In 1991 he joined the Electronic and Telecommunication Research Institute, Seoul, Korea, as a Senior Research Staff. Since 1992, he has been an Associate Professor in the Department of Computer Science and Engineering of the Inha University, Incheon, Korea and since 2001, he is a Professor in the same department and university. His current research interests are pattern recognition, machine intelligence, and parallel computer architecture. dr. rhee is a Member of the IEEE Computer Society and KISS (Korea Information Science Society).     

On the suitability of Biological structure determination by electron microscopy to Grid Computing

The present contribution describes a potential application of Grid Computing in Bioinformatics. High resolution structure determination of biological specimens is critical in BioSciences to understanding the biological function. The problem is computational intensive. Distributed and Grid Computing are thus becoming essential. This contribution analyzes the use of Grid Computing and its potential benefits in the field of electron microscope tomography of biological specimens. Jose-Jesus Fernandez, Ph.D.: He received his M.Sc. and Ph.D. degrees in Computer Science from the University of Granada, Spain, in 1992 and 1997, respectively. He was a Ph.D. student at the Bio-Computing unit of the National Center for BioTechnology (CNB) from the Spanish National Council of Scientific Research (CSIC), Madrid, Spain. He became an Assistant Professor in 1997 and, subsequently, Associate Professor in 2000 in Computer Architecture at the University of Almeria, Spain. He is a member of the supercomputing-algorithms research group. His research interests include high performance computing (HPC), image processing and tomography. Jose-Roman Bilbao-Castro: He received his M.Sc. degree in Computer Science from the University of Almeria in 2001. He is currently a Ph.D. student at the BioComputing unit of the CNB (CSIC) through a Ph.D. CSIC-grant in conjuction with Dept. Computer Architecture at the University of Malaga (Spain). His current research interestsinclude tomography, HPC and distributed and grid computing. Roberto Marabini, Ph.D.: He received the M.Sc. (1989) and Ph.D. (1995) degrees in Physics from the University Autonoma de Madrid (UAM) and University of Santiago de Compostela, respectively. He was a Ph.D. student at the BioComputing Unit at the CNB (CSIC). He worked at the University of Pennsylvania and the City University of New York from 1998 to 2002. At present he is an Associate Professor at the UAM. His current research interests include inverse problems, image processing and HPC. Jose-Maria Carazo, Ph.D.: He received the M.Sc. degree from the Granada University, Spain, in 1981, and got his Ph.D. in Molecular Biology at the UAM in 1984. He left for Albany, NY, in 1986, coming back to Madrid in 1989 to set up the BioComputing Unit of the CNB (CSIC). He was involved in the Spanish Ministry of Science and Technology as Deputy General Director for Research Planning. Currently, he keeps engaged in his activities at the CNB, the Scientific Park of Madrid and Integromics S.L. Immaculada Garcia, Ph.D.: She received her B.Sc. (1977) and Ph.D. (1986) degrees in Physics from the Complutense University of Madrid and University of Santiago de Compostela, respectively. From 1977 to 1987 she was an Assistant professor at the University of Granada, from 1987 to 1996 Associate professor at the University of Almeria and since 1997 she is a Full Professor and head of Dept. Computer Architecture. She is head of the supercomputing-algorithms research group. Her research interest lies in HPC for irregular problems related to image processing, global optimization and matrix computation.     

Dynamic vehicle routing using genetic algorithms

Many difficult combinatorial optimization problems have been modeled as static problems. However, in practice, many problems are dynamic and changing, while some decisions have to be made before all the design data are known. For example, in the Dynamic Vehicle Routing Problem (DVRP), new customer orders appear over time, and new routes must be reconfigured while executing the current solution. Montemanni et al. [1] considered a DVRP as an extension to the standard vehicle routing problem (VRP) by decomposing a DVRP as a sequence of static VRPs, and then solving them with an ant colony system (ACS) algorithm. This paper presents a genetic algorithm (GA) methodology for providing solutions for the DVRP model employed in [1]. The effectiveness of the proposed GA is evaluated using a set of benchmarks found in the literature. Compared with a tabu search approach implemented herein and the aforementioned ACS, the proposed GA methodology performs better in minimizing travel costs. Franklin T. Hanshar is currently a M.Sc. student in the Department of Computing and Information Science at the University of Guelph, Ontario, Canada. He received a B.Sc. degree in Computer Science from Brock University in 2005. His research interests include uncertain reasoning, optimization and evolutionary computation. Beatrice Ombuki-Berman is currently an Associate Professor in the Department of Computer Science at Brock University, Ontario, Canada. She obtained a PhD and ME in Information Engineering from University of The Ryukyus, Okinawa, Japan in 2001 and 1998, respectively. She received a B.Sc. in Mathematics and Computer Science from Jomo Kenyatta University, Nairobi, Kenya. Her primary research interest is evolutionary computation and applied optimization. Other research interests include neural networks, machine learning and ant colony optimization.     

An in-depth analysis of information markets with aggregate uncertainty

The novel idea of setting up Internet-based virtual markets, information markets, to aggregate dispersed information and predict outcomes of uncertain future events has empirically found its way into many domains. But the theoretical examination of information markets has lagged relative to their implementation and use. This paper proposes a simple theoretical model of information markets to understand their information dynamics. We investigate and provide initial answers to a series of research questions that are important to understanding how information markets work, which are: (1) Does an information market converge to a consensus equilibrium? (2) If yes, how fast is the convergence process? (3) What is the best possible equilibrium of an information market? and (4) Is an information market guaranteed to converge to the best possible equilibrium? The authors acknowledge the support of the eBusiness Research Center at the Pennsylvania State University. Yiling Chen is a postdoctoral research scientist at Yahoo! Research, New York. She received her Bachelor of Economics degree in Commodity Science from Renmin University of China, in 1996, and her Master of Economics degree in Finance from Tsinghua University, China, in 1999. She worked for PriceWaterhouse Coopers China as a professional auditor from August 1999 to June 2000. From August 2000 to July 2001, she attended Iowa State University, Ames, IA, as a Ph.D. student in economics. She obtained her Ph.D. in Information Sciences and Technology from the Pennsylvania State University, University Park, PA, in 2005. Her research interests lie on the boarder of computer science, economics, and business, including information markets, auction theory, and machine learning. Tracy Mullen is an assistant professor of information sciences and technology at the Pennsylvania State University, University Park, PA. She has previously worked at Lockheed Martin, Bellcore, and NEC Research. She received her PhD in Computer Science from University of Michigan. Her research interests include information markets, multiagent systems, ecommerce, market-based resource allocation for sensor management, and supply chain simulations using intelligent agents. Her research papers have been published in Decision Support Systems, Electronic Commerce Research, IEEE Computer, ACM Transactions on Internet Technology, Mathematics and Computers in Simulation, and Operating Systems Review, among others. Chao-Hsien Chu is an associate professor of information sciences and technology and the executive director of the Center for Information Assurance at the Pennsylvania State University, University Park, PA. He was previously on the faculty at Iowa State University, Iowa and Baruch College, New York and a visiting professor at University of Tsukuba (Japan) and Hebei University of Technology (China). He is currently on leaves to the Singapore Management University (Singapore) (2005–2006). Dr. Chu received a Ph.D. in Business Administration from Penn State. His current research interests are in communication networks design, information assurance and security (especially in wireless security, intrusion detection, and cyber forensics), intelligent technologies (fuzzy logic, neural network, genetic algorithms, etc.) for data mining (e.g., bioinformatics, privacy preserving) and supply chains integration. His research papers have been published in Decision Sciences, IEEE Transactions on Evolutionary Computation, IIE Transactions, Decision Support Systems, European Journal of Operational Research, Electronic Commerce Research, Expert Systems with Applications, International Journal of Mobile Communications, Journal of Operations Management, International Journal of Production Research, among others. He is currently on the editorial review board for a number of journals.     

InfoFilter: Supporting quality of service for fresh information delivery

With the explosive growth of the Internet and World Wide Web comes a dramatic increase in the number of users that compete for the shared resources of distributed system environments. Most implementations of application servers and distributed search software do not distinguish among requests to different web pages. This has the implication that the behavior of application servers is quite unpredictable. Applications that require timely delivery of fresh information consequently suffer the most in such competitive environments. This paper presents a model of quality of service (QoS) and the design of a QoS-enabled information delivery system that implements such a QoS model. The goal of this development is two-fold. On one hand, we want to enable users or applications to specify the desired quality of service requirements for their requests so that application-aware QoS adaptation is supported throughout the Web query and search processing. On the other hand, we want to enable an application server to customize how it should respond to external requests by setting priorities among query requests and allocating server resources using adaptive QoS control mechanisms. We introduce the Infopipe approach as the systems support architecture and underlying technology for building a QoS-enabled distributed system for fresh information delivery. Ling Liu, Ph.D.: She is an associate professor at the College of Computing, Georgia Institute of Technology. She received her Ph.D. from Tilburg University, The Netherlands in 1993. Her research interests are in the area of large-scale data intensive systems and its applications in distributed, mobile, multimedia, and Internet computing environments. Her work has focused on systems support for creating, searching, manipulating, and monitoring streams of information in wide area networked information systems. She has published more than 70 papers in internal journals or international conferences, and has served on more than 20 program committees in the area of data engineering, databases, and knowledge and information management. Calton Pu, Ph. D.: He is a Professor and John P. Imlay, Jr. Chair in Software at the College of Computing, Georgia Institute of Technology. Calton received his Ph.D. from University of Washington in 1986. He leads the Infosphere expedition project, which is building the system software to support the next generation information flow applications. Infosphere research includes adaptive operating system kernels, communications middleware, and distributed information flow applications. His past research included operating system projects such as Synthetix and Microfeedback, extended transaction projects such as Epsilon Serializability, and Internet data management. He has published more than 125 journal and conference papers, and served on more than 40 program committees. Karsten Schwan, Ph.D.: He is a professor in the College of Computing at the Georgia Institute of Technology. He received the M.Sc. and Ph.D. degrees from Carnegie-Mellon University in Pittsburgh, Pennsylvania. He directs the IHPC project for high performance cluster computing at Georgia Tech. His current research addresses the interactive nature of modern high performance applications (i.e., online monitoring and computational steering), the development of efficient and object-based middleware, the operating system support for distributed and parallel programs, and the online configuration of applications for distributed real-time applications and for communication protocols. Jonathan Walpole, Ph.D.: He is a Professor in the Computer Science and Engineering Department at oregon Graduate Institute of Science and Technology. He received his Ph.D. in Computer Science from Lancaster University, U.K. in 1987. His research interests are in the area of adaptive systems software and its application in distributed, mobile, multimedia computing environments. His work has focused on quality of service specification, adaptive resource management and dynamic specialization for enhanced performance, survivability and evolvability of large software systems, and he has published extensively in these areas.     

Ranking-based evaluation of regression models

We suggest the use of ranking-based evaluation measures for regression models, as a complement to the commonly used residual-based evaluation. We argue that in some cases, such as the case study we present, ranking can be the main underlying goal in building a regression model, and ranking performance is the correct evaluation metric. However, even when ranking is not the contextually correct performance metric, the measures we explore still have significant advantages: They are robust against extreme outliers in the evaluation set; and they are interpretable. The two measures we consider correspond closely to non-parametric correlation coefficients commonly used in data analysis (Spearman's ρ and Kendall's τ); and they both have interesting graphical representations, which, similarly to ROC curves, offer useful various model performance views, in addition to a one-number summary in the area under the curve. An interesting extension which we explore is to evaluate models on their performance in “partially” ranking the data, which we argue can better represent the utility of the model in many cases. We illustrate our methods on a case study of evaluating IT Wallet size estimation models for IBM's customers. Saharon Rosset is Research Staff Member in the Data Analytics Research Group at IBM's T. J. Watson Research Center. He received his B.S. in Mathematics and M.Sc., in Statistics from Tel Aviv University in Israel, and his Ph.D. in Statistics from Stanford University in 2003. In his research, he aspires to develop practically useful predictive modeling methodologies and tools, and apply them to solve problems in business and scientific domains. Currently, his major projects include work on customer wallet estimation and analysis of genetic data. Claudia Perlich has received a M.Sc. in Computer Science from Colorado University at Boulder, a Diploma in Computer Science from Technische Universitaet in Darmstadt, and her Ph.D. in Information Systems from Stern School of Business, New York University. Her Ph.D. thesis concentrated on probability estimation in multi-relational domains that capture information of multiple entity types and relationships between them. Her dissertation was recognized as an additional winner of the International SAP Doctoral Support Award Competition. Claudia joined the Data Analytics Research group at IBM's T.J. Watson Research Center as a Research Staff Member in October 2004. Her research interests are in statistical machine learning for complex real-world domains and business applications. Bianca Zadrozny is currently an associate professor at the Computer Science Department of Federal Fluminense University in Brazil. Her research interests are in the areas of applied machine learning and data mining. She received her B.Sc. in Computer Engineering from the Pontifical Catholic University in Rio de Janeiro, Brazil, and her M.Sc. and Ph.D. in Computer Science from the University of California at San Diego. She has also worked as a research staff member in the data analytics research group at IBM T.J. Watson Research Center.     

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.     

A Metadata Service for Integrated Management of Knowledges Related to Coastal Areas

The research outlined in this paper is part of a wider research program named SYSCOLAG (Coastal and LAGoonal SYStems in Languedoc-Roussillon area, France) dedicated to sustainable coastal management. The main objective of this program is to build up a communication infrastructure to improve the exchange of information and knowledge between the various scientific disciplines involved in the research. In order to ensure the sharing of resources without affecting the autonomy and independance of the partners, we propose a three-level infrastructure (resources, federation and knowledge access) based on a metadata service (using ISO 19115 standard for geographic information metadata) completed by a common vocabulary (ontology).The Syscolag research program (COastal and LAGoonal SYStems) is funded by Languedoc Roussillon authority.Julien Barde is currently a Ph.D. student in Computer Science at the LIRMM (the Computer Science, Robotic and Microelectronic Laboratory of the University of Montpellier II, France) under the guidance of Thérèse Libourel and Pierre Maurel since 2002. He works for a research program of Integrated Coastal Management to improve knowledge sharing between the stakeholders of Languedoc Roussillon coastal area. He has received his engineer/M.Sc. degrees in Oceanology Sciences and Spatial Information Treatment from the National Superior Agronomic School of Rennes (ENSAR, Brittany, France) in 2000 and 2001. He has experience in Computer Science, Remote sensing, GIS and oceanology.Thérèse Libourel is a Senior Lecturer in Computer Science from the Conservatoire National des Arts et Métiers (CNAM), currently at the LIRMM (the Computer Science, Robotic and Microelectronic Laboratory of the University of Montpellier II, France) since 1994. She holds a Ph.D. and a habilitation thesis in Computer Science from the University of Montpellier II (France). Among others, her research interests are oriented towards object oriented design, reuse of software components, object oriented databases and evolution, and data models for spatial and temporal information systems.Pierre Maurel is a research engineer in Cemagref (France). He received his Diploma on Agronomy Engineering from ESAP high school (France) in 1986 and his M.Sc. on quantitative geography in 1990 from Avignon University (France). In the past, he performed research and teaching in satellite image processing and GIS for environmental and water applications. His current scientific interests include the development of methods for the design of multi-partners geographic information systems, the use of metadata within Spatial Data Infrastructures and the integration of Geographic Information technologies to support public participation in the field of Integrated River Basin Management (HarmoniCOP European project).     

Determination of user interfaces in adaptive systems using a rough classification-based method

This paper presents a novel method for user classification in adaptive systems based on rough classification. Adaptive systems could be used in many areas, for example in a user interface construction or e-Learning environments for learning strategy selection. In this paper the adaptation of web-based system user interface is presented. The goal of rough user classification is to select the most essential attributes and their values that group together users who are very much alike concerning the system logic. In order to group users we exploit their usage data taken from the user model of the adaptive web-based system user interface. We presented three basic problems for attribute selection that generates the following partitions: that is included, that includes and that is the closest to the given partition. Ngoc Thanh Nguyen, Ph.D., D.Sc.: He currently works as an associate professor at the Faculty of Computer Science and Management, Wroclaw University of Technology in Poland. He received his diplomas of M.Sc, Ph.D. and D.Sc. in Computer Science in 1986, 1989 and 2002, respectively. Actually, he is working on intelligent technologies for conflict resolution and inconsistent knowledge processing and e-learning methods. His teaching interests consist of database systems and distributed systems. He is a co-editor of 4 special issues in international journals, author of 3 monographs, editor of one book and about 110 other publications (book chapters, journal and refereed conference papers). He is an associate editor of the following journals: “International Journal of Computer Science & Application”; “Journal of Information Knowledge System Management”; and “International Journal of Knowledge-Based & Intelligent Engineering Systems”. He is a member of societies: ACM, IFIP WG 7.2, ISAI, KES International, and WIC. Janusz Sobecki, Ph.D.: He is an Assistant Professor in Institute of Applied Informatics (IAI) at Wroclaw University of Technology (WUT). He received his M. Sc. in Computer Science from Faculty of Computer Science and Management at WUT in 1986 and Ph.D. in Computer Science from Faculty of Electronics at WUT in 1994. For 1986–1996 he was an Assistant at the Department of Information Systems (DIS) at WUT. For 1988–1996 he was also a head of the laboratory at DIS. For 1996–2004 he was an Assistant Professor in DIS and since fall of 2004 at IAI, both at WUT. His research interests include information retrieval, multimedia information systems, system usability and recommender systems. He is on the editorial board of New Generation Computing and was a co-editor of two journal special issues. He is a member of American Association of Machinery.