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

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

CPU load shedding for binary stream joins

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

Achievement of minimized combinatorial test suite for configuration-aware software functional testing using the Cuckoo Search algorithm

ContextSoftware has become an innovative solution nowadays for many applications and methods in science and engineering. Ensuring the quality and correctness of software is challenging because each program has different configurations and input domains. To ensure the quality of software, all possible configurations and input combinations need to be evaluated against their expected outputs. However, this exhaustive test is impractical because of time and resource constraints due to the large domain of input and configurations. Thus, different sampling techniques have been used to sample these input domains and configurations.ObjectiveCombinatorial testing can be used to effectively detect faults in software-under-test. This technique uses combinatorial optimization concepts to systematically minimize the number of test cases by considering the combinations of inputs. This paper proposes a new strategy to generate combinatorial test suite by using Cuckoo Search concepts.MethodCuckoo Search is used in the design and implementation of a strategy to construct optimized combinatorial sets. The strategy consists of different algorithms for construction. These algorithms are combined to serve the Cuckoo Search.ResultsThe efficiency and performance of the new technique were proven through different experiment sets. The effectiveness of the strategy is assessed by applying the generated test suites on a real-world case study for the purpose of functional testing.ConclusionResults show that the generated test suites can detect faults effectively. In addition, the strategy also opens a new direction for the application of Cuckoo Search in the context of software engineering.     

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

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

Scheduling real-time requests in on-demand data broadcast environments

On-demand broadcast is an attractive data dissemination method for mobile and wireless computing. In this paper, we propose a new online preemptive scheduling algorithm, called PRDS that incorporates urgency, data size and number of pending requests for real-time on-demand broadcast system. Furthermore, we use pyramid preemption to optimize performance and reduce overhead. A series of simulation experiments have been performed to evaluate the real-time performance of our algorithm as compared with other previously proposed methods. The experimental results show that our algorithm substantially outperforms other algorithms over a wide range of workloads and parameter settings. The work described in this paper was partially supported by grants from CityU (Project No. 7001841) and RGC CERG Grant No. HKBU 2174/03E. This paper is an extended version of the paper “A preemptive scheduling algorithm for wireless real-time on-demand data broadcast” that appeared in the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications. Victor C. S. Lee received his Ph.D. degree in Computer Science from the City University of Hong Kong in 1997. He is now an Assistant Professor in the Department of Computer Science of the City University of Hong Kong. Dr. Lee is a member of the ACM, the IEEE and the IEEE Computer Society. He is currently the Chairman of the IEEE, Hong Kong Section, Computer Chapter. His research interests include real-time data management, mobile computing, and transaction processing. Xiao Wu received the B.Eng. and M.S. degrees in computer science from Yunnan University, Kunming, China, in 1999 and 2002, respectively. He is currently a Ph.D. candidate in the Department of Computer Science at the City University of Hong Kong. He was with the Institute of Software, Chinese Academy of Sciences, Beijing, China, between January 2001 and July 2002. From 2003 to 2004, he was with the Department of Computer Science of the City University of Hong Kong, Hong Kong, as a Research Assistant. His research interests include multimedia information retrieval, video computing and mobile computing. Joseph Kee-Yin NG received a B.Sc. in Mathematics and Computer Science, a M.Sc. in Computer Science, and a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in the years 1986, 1988, and 1993, respectively. Prof. Ng is currently a professor in the Department of Computer Science at Hong Kong Baptist University. His current research interests include Real-Time Networks, Multimedia Communications, Ubiquitous/Pervasive Computing, Mobile and Location- aware Computing, Performance Evaluation, Parallel and Distributed Computing. Prof. Ng is the Technical Program Chair for TENCON 2006, General Co-Chair for The 11th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2005), Program Vice Chair for The 11th International Conference on Parallel and Distributed Systems (ICPADS 2005), Program Area-Chair for The 18th & 19th International Conference on Advanced Information Networking and Applications (AINA 2004 & AINA 2005), General Co-Chair for The International Computer Congress 1999 & 2001 (ICC’99 & ICC’01), Program Co-Chair for The Sixth International Conference on Real-Time Computing Systems and Applications (RTCSA’99) and General Co-Chair for The 1999 and 2001 International Computer Science Conference (ICSC’99 & ICSC’01). Prof. Ng is a member of the Editorial Board of Journal of Pervasive Computing and Communications, Journal of Ubiquitous Computing and Intelligence, Journal of Embedded Computing, and Journal of Microprocessors and Microsystems. He is the Associate Editor of Real-Time Systems Journal and Journal of Mobile Multimedia. He is also a guest editor of International Journal of Wireless and Mobile Computing for a special issue on Applications, Services, and Infrastructures for Wireless and Mobile Computing. Prof. Ng is currently the Region 10 Coordinator for the Chapter Activities Board of the IEEE Computer Society, and is the Coordinator of the IEEE Computer Society Distinguished Visitors Program (Asia/Pacific). He is a senior member of the IEEE and has been a member of the IEEE Computer Society since 1991. Prof. Ng has been an Exco-member (1993–95), General Secretary (1995–1997), Vice-Chair (1997–1999), Chair (1999–2001) and the Past Chair of the IEEE, Hong Kong Section, Computer Chapter. Prof. Ng received the Certificate of Appreciation for Services and Contribution (2004) from IEEE Hong Kong Section, the Certificate of Appreciation for Leadership and Service (2000–2001) from IEEE Region 10 and the IEEE Meritorious Service Award from IEEE Computer Society at 2004. He is also a member of the IEEE Communication Society, ACM and the Founding Member for the Internet Society (ISOC)-Hong Kong Chapter.     

PAI: Automatic indexing for extracting asserted keywords from a document

This paper proposes an automatic indexing method named PAI (Priming Activation Indexing) that extracts keywords expressing the author’s main point from a document based on the priming effect. The basic idea is that since the author writes a document emphasizing his/her main point, impressive terms born in the mind of the reader could represent the asserted keywords. Our approach employs a spreading activation model without using corpus, thesaurus, syntactic analysis, dependency relations between terms or any other knowledge except for stop-word list. Experimental evaluations are reported by applying PAI to journal/conference papers. Naohiro Matsumura: He received his B.S. and M.S. in Engineering Science from Osaka University in 1998 and 2000. Currently, he is a Ph.D. candidate in Engineering at the University of Tokyo and a research staff of PRESTO of Japan Science and Technology Corporation (2000–). His research interests include chance discovery, computer-mediated communication, and user-oriented data mining/text mining. Yukio Ohsawa, Ph.D.: BS, U. Tokyo, 1990, MS, 1992, DS, 1995. Research associate Osaka U. (1995). Associate prof. Univ. of Tsukuba (1999–) and also researcher of Japan Science and Technology Corp (2000–). He has been working for the program com. of the Workshop on Multiagent and Cooperative Computation, Annual Conf. Japanese Soc. Artificial Intelligence, International Conf. MultiAgent Systems, Discovery Science, Pacific Asia Knowledge Discovery and Data Mining, International Conference on Web Intelligence, etc. He chaired the First International Workshop of Japanese Soc. on Artificial Intelligence, Chance Discovery International Workshop Series and the Fall Symposium on Chance Discovery from AAAI. Guest editor of Special Issues on Chance Discovery for the Journal of Contingencies and Crisis Management, Journal of Japan Society for Fuzzy Theory and intelligent informatics, regular member of editorial board for Japanese Society of Artificial Intelligence. Currently he is authoring book “Chance Discovery” from Springer Verlag, “Knowledge Managament” from Ohmsha etc. Mitsuru Ishizuka, Ph.D.: He is a professor at the Dept. of Infomation and Communication Eng., School of Information Science and Thechnology, the Univ. of Tokyo. Prior to this position, he worked at NTT Yokosuka Lab. and the Institute of Industrial Science, the Univ. of Tokyo. He earned his B.S., M.S. and Ph.D. in electronic engineering from the Univ. of Tokyo. His research interests include artificial intelligence, WWW intelligence, and multimodal lifelike agents. He is a member of IEEE, AAAI, IEICE Japan, IPS Japan, and Japanese Society for AI.     

Cache modeling and optimization for portable devices running MPEG-4 video decoder

There are increasing demands on portable communication devices to run multimedia applications. ISO (an International Organization for Standardization) standard MPEG-4 is an important and demanding multimedia application. To satisfy the growing consumer demands, more functions are added to support MPEG-4 video applications. With improved CPU speed, memory sub-system deficiency is the major barrier to improving the system performance. Studies show that there is sufficient reuse of values for caching that significantly reduce the memory bandwidth requirement for video data. Software decoding of MPEG-4 video data generates much more cache-memory traffic than required. Proper understanding of the decoding algorithm and the composition of its data set is obvious to improve the performance of such a system. The focus of this paper is cache modeling and optimization for portable communication devices running MPEG-4 video decoding algorithm. The architecture we simulate includes a digital signal processor (DSP) for running the MPEG-4 decoding algorithm and a memory system with two levels of caches. We use VisualSim and Cachegrind simulation tools to optimize cache sizes, levels of associativity, and cache levels for a portable device decoding MPEG-4 video. Abu Asaduzzaman is, currently, a PhD candidate in the department of Computer Science and Engineering (CSE), Florida Atlantic University (FAU), Boca Raton, Florida. He received his MS degree in computer engineering from FAU in 1997. Mr. Asaduzzaman worked for ECI Telecom as a software engineer from 1998 to 2001. From 2001 to 2003, he worked for BlueCross and BlueShield of Florida and SunPass (FDoT) as an IT Consultant. Currently, he is working as a research assistant at CSE Dept, FAU. His research interests include cache optimization, architecture exploration, embedded system evaluation, and networks-on-a-chip (NoC). He has published several research papers in these areas. Abu is a member of the honor society of Phi Kappa Phi, Tau Beta Pi, Upsilon Phi Epsilon, and the Association for Computing Machinery (ACM) FAU Chapter. Imad Mahgoub received the MS degree in applied mathematics and MS degree in electrical and computer engineering, both from North Carolina State University, Raleigh in 1983 and 1986 respectively and the PhD degree in computer engineering from the Pennsylvania State University, University Park, PA in 1989. Dr. Mahgoub joined Florida Atlantic University (FAU), Boca Raton, Florida in 1989. Currently he is a full professor of Computer Science and Engineering department and the director of the Mobile Computing Laboratory. His research interests include performance evaluation, mobile computing, sensor networks, and parallel and distributed processing. He has published over 80 research papers in these areas. He is the co-editor of the Mobile Computing Handbook and the Handbook of Sensor Networks. Dr. Mahgoub has served on the program committees of numerous conferences. He has been the vice-chair for the Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS) since 2003. He is a senior member of the IEEE. He is also a member of Tau Beta Pi, Upsilon Pi Epsilon, the IEEE Computer Society, and the ACM.     

Generating semantically valid test inputs using constrained input grammars

ContextGenerating test cases based on software input interface is a black-box testing technique that can be made more effective by using structured input models such as input grammars. Automatically generating grammar-based test inputs may lead to structurally valid but semantically invalid inputs that may be rejected in early semantic error checking phases of a system under test.ObjectiveThis paper aims to introduce a method for specifying a grammar-based input model with the model’s semantic constraints to be used in the generation of positive test inputs. It is also important that the method can generate effective test suites based on appropriate grammar-based coverage criteria.MethodFormal specification of both input structure and input semantics provides the opportunity to use model instantiation techniques to create model instances that satisfy all specified constraints. The input interface of a subject system can be specified using a high-level specification scheme such as attribute grammars, and a transformation function from this scheme to an instantiable formal modeling language can generate the desired model instances.ResultsWe propose a declarative grammar-based input specification method that is based on a variation of attribute grammars and allows the user to specify input constraints in addition to input structure. The model can be instantiated automatically to generate structurally and semantically valid test inputs. The proposed method has the capability to specify test requirements and coverage criteria and use them to generate valid test suites that satisfy test coverage criteria requirements.ConclusionThe work presented in this paper provides a black-box test generation method for grammar-based software inputs that can automatically generate criteria-covering test suites.     

Surface detail capturing for realistic facial animation

In this paper, a facial animation system is proposed for capturing both geometrical information and illumination changes of surface details, called expression details, from video clips simultaneously, and the capture ddata can be widely applied to different 2D face images and 3D face models. While tracking the geometric data, we record the expression details by ratio images. For 2D facial animation synthesis, these ratio images are used to generate dynamic textures. Because a ratio image is obtained via dividing colors of an expressive face by those of a neutral face, pixels with ratio value smaller than one are where a wrinkle or crease appears. The refore, thegradients of the ratio value at each pixel in ratio images are regarded as changes of a face surface, and original normals on the surface can be adjusted according to these gradients. Based on this idea, we can convert the ratio images into a sequence of normal maps and then apply them to animated 3D model rendering. With the expression detail mapping, the resulted facial animations are more life-like and more expressive.     

CCMPerf: A Benchmarking Tool for CORBA Component Model Implementations

Commercial off-the-shelf (COTS) middleware is now widely used to develop distributed real-time and embedded (DRE) systems. DRE systems are themselves increasingly combined to form systems of systems that have diverse quality of service (QoS) requirements. Earlier generations of COTS middleware, such as Object Request Brokers (ORBs) based on the CORBA 2.x standard, did not facilitate the separation of QoS policies from application functionality, which made it hard to configure and validate complex DRE applications. The new generation of component middleware, such as the CORBA Component Model (CCM) based on the CORBA 3.0 standard, addresses the limitations of earlier generation middleware by establishing standards for implementing, packaging, assembling, and deploying component implementations.There has been little systematic empirical study of the performance characteristics of component middleware implementations in the context of DRE systems. This paper therefore provides four contributions to the study of CCM for DRE systems. First, we describe the challenges involved in benchmarking different CCM implementations. Second, we describe key criteria for comparing different CCM implementations using key black-box and white-box metrics. Third, we describe the design of our CCMPerf benchmarking suite to illustrate test categories that evaluate aspects of CCM implementation to determine their suitability for the DRE domain. Fourth, we use CCMPerf to benchmark CIAO implementation of CCM and analyze the results. These results show that the CIAO implementation based on the more sophisticated CORBA 3.0 standard has comparable DRE performance to that of the TAO implementation based on the earlier CORBA 2.x standard.Arvind S. Krishna is a PhD student in the Electrical Engineering and Computer Science Department at Vanderbilt University and a member of the Institute for Software Integrated Systems. He received his MA in management from the Brila Institute for Technology and Science (BITS), Pilani, India and his MS in computer science from University of California, Irvine. His research interests include patterns, real-time Java technologies for Real-Time Corba, model-integrated QA techniques, and tools for partial evaluation and specialization of middleware. He is a student member of the IEEE and ACM. Contact him at the Inst. for Software Integrated Systems, 2015 Terrace Pl., Nashville, TN 37203.Balachandran Natarajan is a senior staff engineer at the Institute for Software Integrated Systems and a PhD student in electrical engineering and computer science at Vanderbilt University. His research focuses on applying patterns, optimization principles, and frameworks to build high-performance, dependable, and real-time distributed systems. He received his MS in computer science from Washington University. Contact him at the Inst. for Software Integrated Systems, 2015 Terrace Pl., Nashville, TN 37203.Aniruddha Gokhale is an assistant professor in the Electrical Engineering and Computer Science Department at Vanderbilt University and a senior research scientist at the Institute for Software Integrated Systems. His research focuses on real-time component middleware optimizations, distributed systems and networks, model-driven software synthesis applied to component middleware-based distributed systems, and distributed resource management. He received his PhD in computer science from Washington University. Contact him at the Inst. for Software Integrated Systems, 2015 Terrace Pl., Nashville, TN 37203.Douglas C. Schmidt is a professor in the Electrical Engineering and Computer Science Department at Vanderbilt University and a senior research scientist at the Institute for Software Integrated Systems. His research interests include patterns, optimization techniques, and empirical analyses of software frameworks and domain-specific modeling environments that facilitate the development of distributed real-time and embedded middleware and applications running over high-speed networks and embedded system interconnects. He received his PhD in information and computer science at the University of California, Irvine. Contact him at the Inst. for Software Integrated Systems, 2015 Terrace Pl., Nashville, TN 37203.Nanbor Wang is a Research Scientist in the Distributed Technologies Group at the Tech-X Corporation in Boulder, Colorado. He received M.S. and Ph.D. degrees in Computer Science from Washington University in St. Louis, Missouri. While working for his degree, he also worked as a Research Associate in the Center of Distributed Object Computing in the Department of Computer Science where he conducted research on design, implementation and analysis of object-oriented and component-based techniques for development of distributed systems and management of extra-functional concerns. Dr. Wangs work currently focuses on developing and applying middleware techniques, such as CORBA and Grid Computing, for enabling distributed and parallel scientific applications, such as, distributed data analysis, remote visualization and collaboration, and, work-flow management for large-scale scientific applications.Gautam H. Thaker was born in Amdavad, India, in 1955. He holds a BSEE (75) and MSEE (77) from Clemson University, Clemson, SC. He spent the 85-86 academic year at M.I.T. as a visiting researcher. His research interests include analysis, design, construction and validation of real-time, command and control systems. In particular he has focused on interactions between operating systems, networking protocols, and middleware technologies.     

Test Resource Partitioning Based on Efficient Response Compaction for Test Time and Tester Channels Reduction

This paper presents a test resource partitioning technique based on an efficient response compaction design called quotient compactor(q-Compactor). Because q-Compactor is a single-output compactor, high compaction ratios can be obtained even for chips with a small number of outputs. Some theorems for the design of q-Compactor are presented to achieve full diagnostic ability, minimize error cancellation and handle unknown bits in the outputs of the circuit under test (CUT). The q-Compactor can also be moved to the load-board, so as to compact the output response of the CUT even during functional testing. Therefore, the number of tester channels required to test the chip is significantly reduced. The experimental results on the ISCAS ‘89 benchmark circuits and an MPEG 2 decoder SoC show that the proposed compactionscheme is very efficient.     

Resource access control for dynamic priority distributed real-time systems

Many of today’s complex computer applications are being modeled and constructed using the principles inherent to real-time distributed object systems. In response to this demand, the Object Management Group’s (OMG) Real-Time Special Interest Group (RT SIG) has worked to extend the Common Object Request Broker Architecture (CORBA) standard to include real-time specifications. This group’s most recent efforts focus on the requirements of dynamic distributed real-time systems. One open problem in this area is resource access synchronization for tasks employing dynamic priority scheduling. This paper presents two resource synchronization protocols that meet the requirements of dynamic distributed real-time systems as specified by Dynamic Scheduling Real-Time CORBA 2.0 (DSRT CORBA). The proposed protocols can be applied to both Earliest Deadline First (EDF) and Least Laxity First (LLF) dynamic scheduling algorithms, allow distributed nested critical sections, and avoid unnecessary runtime overhead. These protocols are based on (i) distributed resource preclaiming that allocates resources in the message-based distributed system for deadlock prevention, (ii) distributed priority inheritance that bounds local and remote priority inversion, and (iii) distributed preemption ceilings that delimit the priority inversion time further. Chen Zhang is an Assistant Professor of Computer Information Systems at Bryant University. He received his M.S. and Ph.D. in Computer Science from the University of Alabama in 2000 and 2002, a B.S. from Tsinghua University, Beijing, China. Dr. Zhang’s primary research interests fall into the areas of distributed systems and telecommunications. He is a member of ACM, IEEE and DSI. David Cordes is a Professor of Computer Science at the University of Alabama; he has also served as Department Head since 1997. He received his Ph.D. in Computer Science from Louisiana State University in 1988, an M.S. in Computer Science from Purdue University in 1984, and a B.S. in Computer Science from the University of Arkansas in 1982. Dr. Cordes’s primary research interests fall into the areas of software engineering and systems. He is a member of ACM and a Senior Member of IEEE.     

Distributed simulation of modular time Petri nets: An approach and a case study exploiting temporal uncertainty

This paper proposes an approach to modular modelling and simulation of complex time-critical systems. The modelling language is represented by Merlin and Farber’s Time Petri Nets (TPNs) augmented with inhibitor arcs and modular constructs borrowed from the Petri Net Markup Language (PNML) interchange format. Analysis techniques depend on Temporal Uncertainty Time Warp (TUTW), a time warp algorithm capable of exploiting temporal uncertainty in general optimistic simulations over a networked context. A key feature of the approach is the fact that TPN models naturally exhibit a certain degree of temporal uncertainty which the TUTW control engine can exploit to achieve good speedup without a loss in the accuracy of the simulation results. The developed TUTW/TPN kernel is demonstrated by modelling and simulation of a real-time system example.A preliminary version of this paper was presented at 38th SCS Annual Simulation Symposium, April 4–6, 2005, San Diego (CA), IEEE Computer Society, pp. 233–240. Franco Cicirelli achieved a PhD in computer science from the University of Calabria (Unical), DEIS—department of electronics informatics and systems science. As a postdoc, he is making research on agent and service paradigms for the development of distributed systems, parallel simulation, Petri nets, distributed measurement systems. He holds a membership with ACM. Angelo Furfaro, PhD, is a computer science assistant professor at Unical, DEIS, teaching object-oriented programming. His research interests are centred on: multi-agent systems, modeling and analysis of time-dependent systems, Petri nets, parallel simulation, verification of real-time systems, distributed measurement systems. He is a member of ACM. Libero Nigro is a full professor of computer science at Unical, DEIS, where he teaches object-oriented programming, software engineering and real-time systems courses. He directs the Software Engineering Laboratory (www.lis.deis.unical.it). His current research interests include: software engineering of time-dependent and distributed systems, real-time systems, Petri nets, modeling and parallel simulation of complex systems, distributed measurement systems. Prof. Nigro is a member of ACM and IEEE.     

On High Dimensional Projected Clustering of Data Streams

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

An attack-norm separation approach for detecting cyber attacks

The two existing approaches to detecting cyber attacks on computers and networks, signature recognition and anomaly detection, have shortcomings related to the accuracy and efficiency of detection. This paper describes a new approach to cyber attack (intrusion) detection that aims to overcome these shortcomings through several innovations. We call our approach attack-norm separation. The attack-norm separation approach engages in the scientific discovery of data, features and characteristics for cyber signal (attack data) and noise (normal data). We use attack profiling and analytical discovery techniques to generalize the data, features and characteristics that exist in cyber attack and norm data. We also leverage well-established signal detection models in the physical space (e.g., radar signal detection), and verify them in the cyberspace. With this foundation of information, we build attack-norm separation models that incorporate both attack and norm characteristics. This enables us to take the least amount of relevant data necessary to achieve detection accuracy and efficiency. The attack-norm separation approach considers not only activity data, but also state and performance data along the cause-effect chains of cyber attacks on computers and networks. This enables us to achieve some detection adequacy lacking in existing intrusion detection systems. Nong Ye is a Professor of Industrial Engineering and an Affiliated Professor of Computer Science and Engineering at Arizona State University (ASU) the Director of the Information Systems Assurance Laboratory at ASU. Her research interests lie in security and Quality of Service assurance of information systems and infrastructures. She holds a Ph.D. degree in Industrial Engineering from Purdue University, West Lafayette, and M.S. and B.S. degrees in Computer Science from the Chinese Academy of Sciences and Peking University in China respectively. She is a senior member of IIE and IEEE, and an Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics and IEEE Transactions on Reliability. Toni Farley is the Assistant Director of the Information and Systems Assurance Laboratory, and a doctoral student of Computer Science at Arizona State University (ASU), Tempe, Arizona. She is studying under a Graduate Fellowship from AT&T Labs-Research. Her research interests include graphs, networks and network security. She holds a B.S. degree in Computer Science and Engineering from ASU. She is a member of IEEE and the IEEE Computer Society. Her email address is toni@asu.edu. Deepak Lakshminarasimhan is a Research Assistant at the Information and Systems Assurance Laboratory, and a Master of Science student of Electrical engineering at Arizona State University (ASU), Tempe, Arizona. His research interests include network security, digital signal processing and statistical data analysis. He holds a B.S degree in Electronics and Communication Engineering from Bharathidasan University in India.     

Combination of clock-state and clock-rate correction in fault-tolerant distributed systems

This paper proposes the integration of internal and external clock synchronization by a combination of a fault-tolerant distributed algorithm for clock state correction with a central algorithm for clock rate correction. By means of hardware and simulation experiments it is shown that this combination improves the precision of the global time base in a distributed single cluster system while reducing the need for high-quality oscillators. Simulation results have shown that the rate-correction algorithm contributes not only in the internal clock synchronization of a single cluster system, but it can be used for external clock synchronization of a multi-cluster system with a reference clock. Therefore, deployment of the rate-correction algorithm integrates internal and external clock synchronization in one mechanism. Experimental results show that a failure in the clock rate correction will not hinder the distributed fault-tolerant clock state synchronization algorithm, since the state correction operates independently from the rate correction. The paper introduces new algorithms and presents experimental results on the achieved improvements in the precision measured in a time-triggered system. Results of simulation experiments of the new algorithms in single-cluster and multi-cluster configurations are also presented. Hermann Kopetz (Fellow, IEEE) received the Ph.D. degree in physics ísub auspiciis praesidentis from the University of Vienna, Vienna, Austria, in 1968. He was Manager of the Computer Process Control Department at Voest Alpine, Linz, Austria, and Professor of Computer Process Control, Technical University of Berlin, Berlin, Germany. He is currently Professor of Real-Time Systems, Vienna University of Technology, Vienna, Austria, and a Visiting Professor at the University of California, Irvine, and the University of California, Santa Barbara. In 1993, he was offered a position as Director of the Max Planck Institute, Saarbrcken, Germany. Prof. Kopetz is the key architect of the Time-Triggered Architecture. Astrit Ademaj (IEEE member) received the Dipl-Ing. degree (1995) at the University of Prishtina, Kosova, and a doctoral degree (2003) in computer science from the Technical University of Vienna. He is currently working as Assistant Professor at the Technical University of Vienna and as a Visiting Lecturer at the University of Prishtina. His research interests are design and validation of communication systems for safety-critical and real-time applications. He is a member of the IEEE Computer Society. Alexander Hanzlik received a diploma (1995) and a doctoral degree (2004) in computer science from the Technical University of Vienna. From 1995 to 1998, he was concerned with voice communication system design for air traffic control for the Service de Navigation Aérienne (STNA). Since 1998, his focus is on embedded systems in the fields of telecommunication, automation and process control. Since 2001, Dr. Hanzlik is a member of the Real-Time Systems Group and works as a research assistant at the Technical University of Vienna. His main research activities deal with fault-tolerant clock synchronization in distributed systems and simulation. Currently, he is working on SIDERA, a simulation model for time-triggered, dependable real-time architectures.     

Real-time classification of variable length multi-attribute motions

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

Decision process modeling across internet and real world by double helical model of chance discovery

A case is presented for the double helical processing of chance discovery — human and an automated data mining system co-work, each progressing spirally toward the creative reconstruction of ideas. Especially, the discovery of what we call chances, significant novel events, is realized in this process. The example shown here is an application to questionnaire analysis for understanding new behaviors of Internet users. Internet users are born and bred with face-to-face human relations in the real world, but their interactions with WWW are distilling new value-criteria, keeping personal real-world senses of rationality, empathy, ethics, etc. In our method for aiding the discovery based on the double-helix model, the in-depth interaction of the Internet, the fundamental (i.e., common both in the Internet and in the real world) characters and the behaviors of people are discussed with revealing unnoticed value-criteria. Yukio Ohsawa, Ph.D.: BS, U. Tokyo, 1990, MS, 1992, DS, 1995. Research associate Osaka U. (1995). Associate prof. Univ. of Tsukuba (1999-) and also researcher of Japan Science and Technology Corp (2000-). He has been working for the program com. of the Workshop on Multiagent and Cooperative Computation, Annual Conf. Japanese Soc. Artificial Intelligence, International Conf. MultiAgent Systems, Discovery Science, Pacific Asia Knowledge Discovery and Data Mining, International Conference on Web Intelligence, etc. He chaired the First International Workshop of Japanese Soc. on Artificial Intelligence, Chance Discovery International Workshop Series and the Fall Symposium on Chance Discovery from AAAI. Guest editor of Special Issues on Chance Discovery for the Journal of Contingencies and Crisis Management, Journal of Japan Society for Fuzzy Theory and intelligent informatics, regular member of editorial board for Japanese Society of Artificial Intelligence. Currently he is authoring book “Chance Discovery” from Springer Verlag, “Knowledge Managament” from Ohmsha etc. Yumiko Nara, Ph.D.: She graduated from Nara Women’s University in 1987 and obtained her Master and Ph.D. degrees from Nara Women’s University respectively in 1993 and 1996. From 1987 through 1990 she worked for Sumitomo Bank. She is at Osaka Kyoiku University as lecturer (1997–2001) and as associate professor (2002-). She serves as a member of The Japan Sociological Society, The Japan Association for Social and Economic Systems Studies, The Japan Society of Home Economics, and The Japan Risk Management Society. She is an editorial committee member of the journal of Social and Economic Systems Studies (2001-), and a council member of The Japan Risk Management Society (1997-). In 1997, she received research awards from The Japan Society of Home Economics and The Japan Risk Management Society for studies on risk management.