Fast evaluation of Bounded Slice-line Grid

Bounded Slice-line Grid (BSG) is an elegant representation of block placement, because it is very intuitionistic and has the advantage of handling various placement constraints. However, BSG has attracted little attention because its evaluation is very time-consuming. This paper proposes a simple algorithm independent of the BSG size to evaluate the BSG representation in O(nloglogn) time, where n is the number of blocks. In the algorithm, the BSG-rooms are assigned with integral coordinates firstly, and then a linear sorting algorithm is applied on the BSG-rooms where blocks are assigned to compute two block sequences, from which the block placement can be obtained in O(n log logn) time. As a consequence, the evaluation of the BSG is completed in O(nloglogn) time, where n is the number of blocks. The proposed algorithm is much faster than the previous graph-based O(n^2) algorithm. The experimental results demonstrate the efficiency of the algorithm.     

Query indexing with containment-encoded intervals for efficient stream processing

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

ARMiner: A Data Mining Tool Based on Association Rules

In this paper,ARM iner,a data mining tool based on association rules,is introduced.Beginning with the system architecture,the characteristics and functions are discussed in details,including data transfer,concept hierarchy generalization,mining rules with negative items and the re-development of the system.An example of the tool‘s application is also shown.Finally,Some issues for future research are presented.     

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.     

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

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

Finding Narrow Passages with Probabilistic Roadmaps: The Small-Step Retraction Method

Probabilistic Roadmaps (PRM) have been successfully used to plan complex robot motions in configuration spaces of small and large dimensionalities. However, their efficiency decreases dramatically in spaces with narrow passages. This paper presents a new method—small-step retraction—that helps PRM planners find paths through such passages. This method consists of slightly “fattening” robot's free space, constructing a roadmap in fattened free space, and finally repairing portions of this roadmap by retracting them out of collision into actual free space. Fattened free space is not explicitly computed. Instead, the geometric models of workspace objects (robot links and/or obstacles) are “thinned” around their medial axis. A robot configuration lies in fattened free space if the thinned objects do not collide at this configuration. Two repair strategies are proposed. The “optimist” strategy waits until a complete path has been found in fattened free space before repairing it. Instead, the “pessimist” strategy repairs the roadmap as it is being built. The former is usually very fast, but may fail in some pathological cases. The latter is more reliable, but not as fast. A simple combination of the two strategies yields an integrated planner that is both fast and reliable. This planner was implemented as an extension of a pre-existing single-query PRM planner. Comparative tests show that it is significantly faster (sometimes by several orders of magnitude) than the pre-existing planner. Mitul Saha received the B.S. degree from the Indian Institute of Technology, Kanpur, India, in 2001 and the M.S. degree from the Computer Science Department at Stanford University, Stanford, CA, in 2005. He is currently pursuing the Ph.D. degree in mechanical engineering at Stanford University. His research interests include motion planning, computer vision, graphics, and structural biology. Jean-Claude Latombe graduated in electrical and computer engineering from the National Polytechnic Institute of Grenoble, France, in 1970. He received the M.S. degree in electrical engineering from the National Polytechnic Institute of Grenoble in 1972, and the PhD degree in computer science from the University of Grenoble in 1977. He joined the Department of Computer Science at Stanford University in 1987, where he currently is the Kumagai Professor in the School of Engineering. He does research in the general areas of artificial intelligence, robotics, and geometric computing. He is particularly interested in motion planning, computational biology, and computer-assisted surgery. Yu-Chi Chang is a Ph.D. candidate in the Mechanical Engineering at Stanford University. Yu-Chi received the B.Sc. in Mechanical Engineering and the M.Sc. in Material Science from National Taiwan University, Taiwan, and the M.Sc. in Mechanical Engineering from Stanford University, United States. His current research interests include robust design and statistical analysis for manufacturing system. Friedrich Prinz is the Rodney H. Adams Professor of Engineering and Professor of Mechanical Engineering and Materials Science and Engineering, Stanford University. Professor Prinz received his Ph.D. degree in Physics from the University of Vienna in 1975. He has been active in synergistic activities with organizations like the National Research Council Committees, the Japanese Technology Evaluation Center and World Technology Evaluation Center, as well as Portuguese Science and Technology Foundation. He was elected to the Austrian Academy of Science (foreign member), Vienna, Austria in 1996. Dr. Prinz's current research activities address a wide range of problems related to design and rapid prototyping of organic and inorganic devices. His current work focuses on the fabrication and physics of fuel cells as well as the creation of biological cell structures. His group uses atomic force microscopy and impedance spectroscopy to characterize the behavior of electrochemical systems with micro and nano-scale dimensions.     

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

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

Self-stabilizing depth-first token circulation on networks

Summary This paper proposes a self-stabilizing protocol which circulates a token on a connected network in nondeterministic depth-first-search order, rooted at a special node. Starting with any initial state in which the network may have no token at all or more than one token, the protocol eventually makes the system stabilize in states having exactly one circulating token. With a slight modification to the protocol —by removing nondeterminism in the search — a depth-first-search tree on the network can be constructed. The proposed protocol runs on systems that allow parallel operations. Shing-Tsaan Huang was born in Taiwan on September 4, 1949. He got his Ph.D. degree in 1985 from Department of Computer Science, University of Maryland at College Park. Before he pursued his Ph.D. degree, he had worked several years in the computer industry in Taiwan. Professor Huang is currently the chairman of the Department of Computer Science, Tsing Hua University, Taiwan, Republic of China. His research interests include interconnection networks, operating systems and distributed computing. He is a senior member of the IEEE Computer Society and a member of the Association for Computing Machinery. Nian-Shing Chen was born in Taiwan on October 21, 1961. He received his Ph.D. degree in computer science from National Tsing Hua University in 1990. Dr. Chen is currently an associate professor with the Department of Information Management at Sun Yat-Sen University of Taiwan. His research interests include distributed systems, computer networks, computer viruses and expert systems. He is a member of IEEE and Phi Tau Phi honorary society.This research is supported by National Science Council of the Republic of China under the contract NSC81-0408-E-007-05 and NSC82-0408-E-007-027     

Anti-aliased rendering of water surface

Water surface is one of the most important components of landscape scenes. When rendering spacious water surface such as that of the lakes and reservoirs, aliasing and/or moiré artifacts frequently occur in the regious far from the viewpoint. This is because water surface consists of stochastic water waves which are usually modeled by periodic bump mapping. The incident rays on the water surface are actually scattered by the bumped waves, and the reflected rays at each sample point are distributed in a solid angle. To get rid of the artifacts of moiré pattern, we estimate this solid angle of reflected rays and trace these rays. An image-based accelerating method is adopted so that the contribution of each reflected ray can be quickly obtained without elaborate intersection calculation. We also demonstrate anti-aliased shadows of sunlight and skylight on the water surface. Both the rendered images and animations show excellent effects on the water surface of a reservoir. The first, third and fifth co-authors were partially supported by the National Natural Science Foundation of China (Grant Nos. 60021201 and 60373035), Key Research Project of Ministry of Education (Grant No.01094) and the National Grand Fundamental Research 973 Program of China (Grant No.2002CB312102). Xue-Ying Qin is an associated professor of State Key Laboratory of CAD&CG, Zhejiang University. She received her Ph.D. degree from Hiroshima University in 2001, B.S. and M.S. degrees in Mathematics from Peking University in 1988 and from Zhejiang University in 1991, respectively. Her research interests include computer graphics, visions and image processing. Eihachiro Nakamae is currently Chairman of Sanei Co. He was granted the title of emeritus professor from both Hiroshima University and Hiroshima Institute of Technology. He was appointed as a researcher associate at Hiroshima University in 1956, a professor from 1968 to 1992 and an associated researcher at Clarkson College of Technology, Potsdam, N.Y., from 1973 to 1974. He was a professor at Hiroshima Prefectural University from 1992 to 1995 and a professor at Hiroshima Institute of Technology from a996 to the end of March 1999. He received his B.E., M.E., and Ph.D. degrees in electrical engineering in 1954, 1956, and 1967 from Waseda University. His research interests include computer graphics, image processing and electric machinery. He is a member of IEEE, ACM, CGS, Eurographics, IEE of Japan, and IPS of Japan. Wei Hua received his Ph.D. degree in applied mathematics from Zhejiang University in 2002. He joined the CAD&CG State Key Lab in 2002. His main interests include real-time simulation and rendering, virtual reality and software engineering. Yasuo Nagai is now an associate professor of Hiroshima Institute of Technology. He was appointed a researcher associate at Hiroshima Institute of Technology in 1965, and an associate professor in 1984. His research interests include computer graphics and image processing. He is a member of IEE, IEICE, IPSJ, and ITE of Japan. Qun-Sheng Peng was born in 1947. He received his Ph.D. degree in computer science from the University of East Anglia, U.K., in 1983. He is a professor and his research interests include computer graphics, computer animation, virtual reality, and point-based modeling and rendering.     

Improving WCET by applying worst-case path optimizations

It is advantageous to perform compiler optimizations that attempt to lower the worst-case execution time (WCET) of an embedded application since tasks with lower WCETs are easier to schedule and more likely to meet their deadlines. Compiler writers in recent years have used profile information to detect the frequently executed paths in a program and there has been considerable effort to develop compiler optimizations to improve these paths in order to reduce the average-case execution time (ACET). In this paper, we describe an approach to reduce the WCET by adapting and applying optimizations designed for frequent paths to the worst-case (WC) paths in an application. Instead of profiling to find the frequent paths, our WCET path optimization uses feedback from a timing analyzer to detect the WC paths in a function. Since these path-based optimizations may increase code size, the subsequent effects on the WCET due to these optimizations are measured to ensure that the worst-case path optimizations actually improve the WCET before committing to a code size increase. We evaluate these WC path optimizations and present results showing the decrease in WCET versus the increase in code size. A preliminary version of this paper entitled “Improving WCET by optimizing worst-case paths” appeared in the 2005 Real-Time and Embedded Technology and Applications Symposium. Wankang Zhao received his PhD in Computer Science from Florida State University in 2005. He was an associate professor in Nanjin University of Post and Telecommunications. He is currently working for Datamaxx Corporation. William Kreahling received his PhD in Computer Science from Florida State University in 2005. He is currently an assistant professor in the Math and Computer Science department at Western Carolina University. His research interests include compilers, computer architecture and parallel computing. David Whalley received his PhD in CS from the University of Virginia in 1990. He is currently the E.P. Miles professor and chair of the Computer Science department at Florida State University. His research interests include low-level compiler optimizations, tools for supporting the development and maintenance of compilers, program performance evaluation tools, predicting execution time, computer architecture, and embedded systems. Some of the techniques that he developed for new compiler optimizations and diagnostic tools are currently being applied in industrial and academic compilers. His research is currently supported by the National Science Foundation. More information about his background and research can be found on his home page, http://www.cs.fsu.edu/∼whalley. Dr. Whalley is a member of the IEEE Computer Society and the Association for Computing Machinery. Chris Healy earned a PhD in computer science from Florida State University in 1999, and is currently an associate professor of computer science at Furman University. His research interests include static and parametric timing analysis, real-time and embedded systems, compilers and computer architecture. He is committed to research experiences for undergraduate students, and his work has been supported by funding from the National Science Foundation. He is a member of ACM and the IEEE Computer Society. Frank Mueller is an Associate Professor in Computer Science and a member of the Centers for Embedded Systems Research (CESR) and High Performance Simulations (CHiPS) at North Carolina State University. Previously, he held positions at Lawrence Livermore National Laboratory and Humboldt University Berlin, Germany. He received his Ph.D. from Florida State University in 1994. He has published papers in the areas of embedded and real-time systems, compilers and parallel and distributed systems. He is a founding member of the ACM SIGBED board and the steering committee chair of the ACM SIGPLAN LCTES conference. He is a member of the ACM, ACM SIGPLAN, ACM SIGBED and the IEEE Computer Society. He is a recipient of an NSF Career Award.     

Generation of Discrete Bicubic G^1 B-Spline Ship Hullform Surfaces from a Given Curve Network Using Virtual Iso-Parametric Curves

We propose a method that automatically generates discrete bicubic G^1 continuous B-spline surfaces that interpolate the curve network of a ship huliform.First,the curves in the network are classified into two types;boundary curves and ＂reference curves＂,The boundary curves correspond to a set of rectangular（or triangular）topological type that can be representes with tensot-product （or degenerate）B-spline surface patches.Next,in the interior of the patches,surface fitting points and cross boundary derivatives are estimated from the reference curves by constructing ＂virtual＂isoparametric curves.Finally,a discrete G^1 continuous B-spline surface is gencrated by a surface fitting algorithm.Several smooth ship hullform surfaces generated from curve networks corresponding to actual ship hullforms demonstrate the quality of the method.     

Internet Network Resource Information Model

The foundation of any network management systems is a database that contains information about the network resources relevant to the management tasks.A network information model is an abstraction of network resources,including both managed resources and managing resources,In the SNMP-based management framework,management information is defined almost exclusively from a “Device“ viewpoint,namely managing a network is equivalent to managing a collection of individual nodes.Aiming at making use of recent advances in distributed computing and in object-oriented analysis and design,the Internet management architecture can also be based on the Open Distributed Processing Reference Model(RM-ODP).The purpose of this article is to provide an Internet Network Resource Information Model.First,a layered management information architecture will be discussed.Then the Internet Network resource information model is presented.The information model is specified using object-Z.     

Improving retrieval performance by region constraints and relevance feedback

In this paper, region features and relevance feedback are used to improve the performance of CBIR. Unlike existing region-based approaches where either individual regions are used or only simple spatial layout is modeled, the proposed approach simultaneously models both region properties and their spatial relationships in a probabilistic framework. Furthermore, the retrieval performance is improved by an adaptive filter based relevance feedback. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.     

Finite-horizon scheduling of radar dwells with online template construction

Timing constraints for radar tasks are usually specified in terms of the minimum and maximum temporal distance between successive radar dwells. We utilize the idea of feasible intervals for dealing with the temporal distance constraints. In order to increase the freedom that the scheduler can offer a high-level resource manager, we introduce a technique for nesting and interleaving dwells online while accounting for the energy constraint that radar systems need to satisfy. Further, in radar systems, the task set changes frequently and we advocate the use of finite horizon scheduling in order to avoid the pessimism inherent in schedulers that assume a task will execute forever. The combination of feasible intervals and online dwell packing allows modular schedule updates whereby portions of a schedule can be altered without affecting the entire schedule, hence reducing the complexity of the scheduler. Through extensive simulations we validate our claims of providing greater scheduling flexibility without compromising on performance when compared with earlier work based on templates constructed offline. We also evaluate the impact of two parameters in our scheduling approach: the template length (or the extent of dwell nesting and interleaving) and the length of the finite horizon. Sathish Gopalakrishnan is a visting scholar in the Department of Computer Science, University of Illinois at Urbana-Champaign, where he defended his Ph.D. thesis in December 2005. He received an M.S. in Applied Mathematics from the University of Illinois in 2004 and a B.E. in Computer Science and Engineering from the University of Madras in 1999. Sathish’s research interests concern real-time and embedded systems, and the design of large-scale reliable systems. He received the best student paper award for his work on radar dwell scheduling at the Real-Time Systems Symposium 2004. Marco Caccamo graduated in computer engineering from the University of Pisa in 1997 and received the Ph.D. degree in computer engineering from the Scuola Superiore S. Anna in 2002. He is an Assistant Professor of the Department of Computer Science at the University of Illinois. His research interests include real-time operating systems, real-time scheduling and resource management, wireless sensor networks, and quality of service control in next generation digital infrastructures. He is recipient of the NSF CAREER Award (2003). He is a member of ACM and IEEE. Chi-Sheng Shih is currently an assistant professor at the Graduate Institute of Networking and Multimedia and Department of Computer Science and Information Engineering at National Taiwan University since February 2004. He received the B.S. in Engineering Science and M.S. in Computer Science from National Cheng Kung University in 1993 and 1995, respectively. In 2003, he received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. His main research interests are embedded systems, hardware/software codesign, real-time systems, and database systems. Specifically, his main research interests focus on real-time operating systems, real-time scheduling theory, embedded software, and software/hardware co-design for system-on-a-chip. Chang-Gun Lee received the B.S., M.S. and Ph.D. degrees in computer engineering from Seoul National University, Korea, in 1991, 1993 and 1998, respectively. He is currently an Assistant Professor in the Department of Electrical Engineering, Ohio State University, Columbus. Previously, he was a Research Scientist in the Department of Computer Science, University of Illinois at Urbana-Champaign from March 2000 to July 2002 and a Research Engineer in the Advanced Telecomm. Research Lab., LG Information & Communications, Ltd. from March 1998 to February 2000. His current research interests include real-time systems, complex embedded systems, QoS management, and wireless ad-hoc networks. Chang-Gun Lee is a member of the IEEE Computer Society. Lui Sha graduated with the Ph.D. degree from Carnegie-Mellon University in 1985. He was a Member and then a Senior Member of Technical Staff at Software Engineering Institute (SEI) from 1986 to 1998. Since Fall 1998, he has been a Professor of Computer Science at the University of Illinois at Urbana Champaign, and a Visiting Scientist of the SEI. He was the Chair of IEEE Real Time Systems Technical Committee from 1999 to 2000, and has served on its Executive Committee since 2001. He was a member of National Academy of Science’s study group on Software Dependability and Certification from 2004 to 2005, and is an IEEE Distinguished Visitor (2005 to 2007). Lui Sha is a Fellow of the IEEE and the ACM.     

Privacy preservation for data cubes

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

Secure software infrastructure in the internet age

The rapid growth and penetration of the Internet are now leading us to a world where networks are ubiquitous and everything is connected. Breaking the distance barrier by the ubiquitous connection, however, is a two-edged sword. Our network infrastructure today is still fragile and thus “everything is connected” may simply mean “everything can be attacked from whatever place on the earth.” In this paper, we first point out the importance and inherent problems of software systems that underlay open and extensible networks, especially the Internet. We put emphasis on software since software vulnerabilities account for most attacks, incidents, or even disasters on the Internet today. Next we present general ideas of promising techniques in defense of software systems, including theoretical, language-based, and runtime solutions. Finally, we show our experience in developing a secure mail system. Etsuya Shibayama, D.Sc.: He is a professor of the Graduate School of Information Science and Engineering at Tokyo Institute of Technology. He received B.Sc. and M.Sc. in mathematical sciences from Kyoto University in 1981 and 1983, respectively, and D.Sc. in information science from the University of Tokyo in 1991. He is interested in various topics in software including design and implementation of textual and visual programming languages, system software, and user interface software. Recently, he has been doing research on language-based software security and methodologies for building secure software. Akinori Yonezawa, Ph.D.: He is a Professor of computer science at Department of Computer Science, the University of Tokyo. He received his Ph.D. in Computer Science form the Massachusetts Institute of Technology in 1977. His current major research interests are in the areas of concurrent/parallel computation models, programming languages, object-oriented computing and distributed computing. He is the designer of and object-oriented concurrent language ABCL/1 and the editor of several books and served as an associate editor of ACM Transaction of Programming Language and Systems (TOPLAS). Since 1998, he has been an ACM Fellow.     

Image hiding scheme using modulus function and optimal substitution table

The simple least-significant-bit (LSB) substitution technique is the easiest way to embed secret data in the host image. To avoid image degradation of the simple LSB substitution technique, Wang et al. proposed a method using the substitution table to process image hiding. Later, Thien and Lin employed the modulus function to solve the same problem. In this paper, the proposed scheme combines the modulus function and the optimal substitution table to improve the quality of the stego-image. Experimental results show that our method can achieve better quality of the stego-image than Thien and Lin’s method does. The text was submitted by the authors in English. Chin-Shiang Chan received his BS degree in Computer Science in 1999 from the National Cheng Chi University, Taipei, Taiwan and the MS degree in Computer Science and Information Engineering in 2001 from the National Chung Cheng University, ChiaYi, Taiwan. He is currently a Ph.D. student in Computer Science and Information Engineering at the National Chung Cheng University, Chiayi, Taiwan. His research fields are image hiding and image compression. Chin-Chen Chang received his BS degree in applied mathematics in 1977 and his MS degree in computer and decision sciences in 1979, both from the National Tsing Hua University, Hsinchu, Taiwan. He received his Ph.D. in computer engineering in 1982 from the National Chiao Tung University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of Computer Engineering at the National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute of Computer Science and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked as a professor in the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan. Dr. Chang is a Fellow of IEEE, a Fellow of IEE and a member of the Chinese Language Computer Society, the Chinese Institute of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests include computer cryptography, data engineering, and image compression. Yu-Chen Hu received his Ph.D. degree in Computer Science and Information Engineering from the Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan in 1999. Dr. Hu is currently an assistant professor in the Department of Computer Science and Information Engineering, Providence University, Sha-Lu, Taiwan. He is a member of the SPIE society and a member of the IEEE society. He is also a member of the Phi Tau Phi Society of the Republic of China. His research interests include image and data compression, information hiding, and image processing.     

BCL—3：Aigh Performance Basic Communication Protocol for Commodity Superserver DAWNING—3000

This paper introduces the design and implemetation of BCL-3,a high performance low-level communication software running on a cluster of SMPs(CLUMPS) called DAWNING-3000,BCL-3 provides flexible and sufficient functionality to fulfill the communication requirements of fundamental system software developed for DAWNING-3000 while guaranteeing security,scalability,and reliability,Important features of BCL-3 are presented in the paper,including special support for SMP and heterogeneous network environment,semiuser-level communication,reliable and ordered data transfer and scalable flow control,The performance evaluation of BCL-3 over Myrinet is also given.     

P transducers

We introduce in this paper four classes of P transducers: arbitrary, initial, isolated arbitrary, isolated and initial. The first two classes are universal, they can compute the same word functions as Turing machines, the latter two are incomparable with finite state sequential transducers, generalized or not. We study the effect of the composition, and show that iteration increases the power of these latter classes, also leading to a new characterization of recursively enumerable languages. The “Sevilla carpet” of a computation is defined for P transducers, giving a representation of the control part for these P transducers. Gabriel Ciobanu, Ph.D.: He has graduated from the Faculty of Mathematics, “A.I.Cuza” University of Iasi, and received his Ph.D. from the same university. He is a senior researcher at the Institute of Computer Science of the Romanian Academy. He has wide-ranging interests in computing including distributed systems and concurrency, computational methods in biology, membrane computing, and theory of programming (semantics, formal methods, logics, verification). He has published around 90 papers in computer science and mathematics, a book on programming semantics and a book on network programming. He is a co-editor of three volumes. He has visited various universities in Europe, Asia, and North America, giving lectures and invited talks. His webpage is http://www.info.uaic.ro/gabriel Gheorghe Păun, Ph.D.: He has graduated from the Faculty of Mathematics, University of Bucharest, in 1974 and received his Ph.D. from the same university in 1977. Curently he works as senior researcher in the Institute of Mathematics of the Romanian Academy, as well as a Ramon y Cajal researcher in Sevilla University, Spain. He has repeatedly visited numerous universities in Europe, Asia, and North America. His main research areas are formal language theory and its applications, computational linguistics, DNA computing, and membrane computing (a research area initiated by him). He has published over 400 research papers (collaborating with many researchers worldwide), has lectured at over 100 universities, and gave numerous invited talks at recognized international conferences. He has published 11 books in mathematics and computer science, has edited about 30 collective volumes, and also published many popular science books and books on recreational mathematics (games). He is on the editorial boards of fourteen international journals in mathematics, computer science, and linguistics, and was/is involved in the program/steering/organizing commitees for many recognized conferences and workshops. In 1997 he was elected a member of the Romanian Academy. Gheorghe Ştefănescu, Ph.D.: He received his B.Sc./M.Sc./Ph.D. degrees in Computer Science from the University of Bucharest. Currently, he is a Professor of Computer Science at the University of Bucharest and a Senior Fellow at the National University of Singapore. Previously, he was a researcher at the Institute of Mathematics of the Romanian Academy and has held visiting positions in The Netherlands, Germany, and Japan. His current research focuses on formal methods in computer science, particularly on process and network algebras, formal methods for interactive, real-time, and object-oriented systems. Some of his results may be found in his book on “Network Algebra,” Springer, 2000.