Finding the most unusual time series subsequence: algorithms and applications

In this work we introduce the new problem of finding time seriesdiscords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While discords have many uses for data mining, they are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence) unlike most anomaly detection algorithms that typically require many parameters. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is three to four orders of magnitude faster than brute force, while guaranteed to produce identical results. We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms, space telemetry, respiration physiology, anthropological and video datasets. Eamonn Keogh is an Assistant Professor of computer science at the University of California, Riverside. His research interests include data mining, machine learning and information retrieval. Several of his papers have won best paper awards, including papers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF Career Award for “Efficient discovery of previously unknown patterns and relationships in massive time series databases.” Jessica Lin is an Assistant Professor of information and software engineering at George Mason University. She received her Ph.D. from the University of California, Riverside. Her research interests include data mining and informational retrieval. Sang-Hee Lee is a paleoanthropologist at the University of California, Riverside. Her research interests include the evolution of human morphological variation and how different mechanisms (such as taxonomy, sex, age, and time) explain what is observed in fossil data. Dr. Lee obtained her Ph.D. in anthropology from the University of Michigan in 1999. Helga Van Herle is an Assistant Clinical Professor of medicine at the Division of Cardiology of the Geffen School of Medicine at UCLA. She received her M.D. from UCLA in 1993; completed her residency in internal medicine at the New York Hospital (Cornell University, 1993–1996) and her cardiology fellowship at UCLA (1997–2001). Dr. Van Herle holds a M.Sc. in bioengineering from Columbia University (1987) and a B.Sc. in Chemical Engineering from UCLA (1985)     

Geometric Analysis of Particle Motion in a Vector Image Field

This paper proposes a geometrical model for the Particle Motion in a Vector Image Field (PMVIF) method. The model introduces a c-evolute to approximate the edge curve in the gray-level image. The c-evolute concept has three major novelties: (1) The locus of Particle Motion in a Vector Image Field (PMVIF) is a c-evolute of image edge curve; (2) A geometrical interpretation is given to the setting of the parameters for the method based on the PMVIF; (3) The gap between the image edge’s critical property and the particle motion equations appeared in PMVIF is padded. Our experimental simulation based on the image gradient field is simple in computing and robust, and can perform well even in situations where high curvature exists. Chenggang Lu received his Bachelor of Science and PhD degrees from Zhejiang University in 1996 and 2003, respectively. Since 2003, he has been with VIA Software (Hang Zhou), Inc. and Huawei Technology, Inc. His research interests include image processing, acoustic signaling processing, and communication engineering. Zheru Chi received his BEng and MEng degrees from Zhejiang University in 1982 and 1985 respectively, and his PhD degree from the University of Sydney in March 1994, all in electrical engineering. Between 1985 and 1989, he was on the Faculty of the Department of Scientific Instruments at Zhejiang University. He worked as a Senior Research Assistant/Research Fellow in the Laboratory for Imaging Science and Engineering at the University of Sydney from April 1993 to January 1995. Since February 1995, he has been with the Hong Kong Polytechnic University, where he is now an Associate Professor in the Department of Electronic and Information Engineering. Since 1997, he has served on the organization or program committees for a number of international conferences. His research interests include image processing, pattern recognition, and computational intelligence. Dr. Chi has authored/co-authored one book and nine book chapters, and published more than 140 technical papers. Gang Chen received his Bachelor of Science degree from Anqing Teachers College in 1983 and his PhD degree in the Department of Applied Mathematics at Zhejiang University in 1994. Between 1994 and 1996, he was a postdoctoral researcher in electrical engineering at Zhejiang University. From 1997 to 1999, he was a visiting researcher in the Institute of Mathematics at the Chinese University of Hong Kong and the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University. Since 2001, he has been a Professor at Zhejiang University. He has been the Director of the Institute of DSP and Software Techniques at Ningbo University since 2002. His research interests include applied mathematics, image processing, fractal geometry, wavelet analysis and computer graphics. Prof. Chen has co-authored one book, co-edited five technical proceedings and published more than 80 technical papers. (David) Dagan Feng received his ME in Electrical Engineering & Computing Science (EECS) from Shanghai JiaoTong University in 1982, MSc in Biocybernetics and Ph.D in Computer Science from the University of California, Los Angeles (UCLA) in 1985 and 1988 respectively. After briefly working as Assistant Professor at the University of California, Riverside, he joined the University of Sydney at the end of 1988, as Lecturer, Senior Lecturer, Reader, Professor and Head of Department of Computer Science/School of Information Technologies, and Associate Dean of Faculty of Science. He is Chair-Professor of Information Technology, Hong Kong Polytechnic University; Honorary Research Consultant, Royal Prince Alfred Hospital, the largest hospital in Australia; Advisory Professor, Shanghai JiaoTong University; Guest Professor, Northwestern Polytechnic University, Northeastern University and Tsinghua University. His research area is Biomedical & Multimedia Information Technology (BMIT). He is the Founder and Director of the BMIT Research Group. He has published over 400 scholarly research papers, pioneered several new research directions, made a number of landmark contributions in his field with significant scientific impact and social benefit, and received the Crump Prize for Excellence in Medical Engineering from USA. More importantly, however, is that many of his research results have been translated into solutions to real-life problems and have made tremendous improvements to the quality of life worldwide. He is a Fellow of ACS, HKIE, IEE, IEEE, and ATSE, Special Area Editor of IEEE Transactions on Information Technology in Biomedicine, and is the current Chairman of IFAC-TC-BIOMED.     

Adaptive component composition and load balancing for distributed stream processing applications

Providing real-time and QoS support to stream processing applications running on top of large-scale overlays is challenging due to the inherent heterogeneity and resource limitations of the nodes and the multiple QoS demands of the applications that must concurrently be met. In this paper we propose an integrated adaptive component composition and load balancing mechanism that (1) allows the composition of distributed stream processing applications on the fly across a large-scale system, while satisfying their QoS demands and distributing the load fairly on the resources, and (2) adapts dynamically to changes in the resource utilization or the QoS requirements of the applications. Our extensive experimental results using both simulations as well as a prototype deployment illustrate the efficiency, performance and scalability of our approach.

Mining partial periodic correlations in time series

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

A geometrical solution to time series searching invariant to shifting and scaling

The technique of searching for similar patterns among time series data is very useful in many applications. The problem becomes difficult when shifting and scaling are considered. We find that we can treat the problem geometrically and the major contribution of this paper is that a uniform geometrical model that can analyze the existing related methods is proposed. Based on the analysis, we conclude that the angle between two vectors after the Shift-Eliminated Transformation is a more intrinsical similarity measure invariant to shifting and scaling. We then enhance the original conical index to adapt to the geometrical properties of the problem and compare its performance with that of sequential search and R^*-tree. Experimental results show that the enhanced conical index achieves larger improvement on R^*-tree and sequential search in high dimension. It can also keep a steady performance as the selectivity increases. Part of the result related to the geometrical model has been published in the Proceedings of the 18th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp 237–248. Mi Zhou was born in China. He received his BS and MS degrees in computer science from the Northeastern University, China, in 1999 and 2002, respectively. He is currently pursuing the Ph D degree in the Computer Science and Engineering Department, The Chinese University of Hong Kong. His research interests include indexing of time series data, high-dimensional index, and sensor network. Man-Hon Wong received his BSc and MPhil degrees from The Chinese University of Hong Kong in 1987 and 1989 respectively. He then went to University of California at Santa Barbara where he got the PhD degree in 1993. Dr. Wong joined The Chinese University of Hong Kong in August 1993 as an assistant professor. He was promoted to associate professor in 1998. His research interests include transaction management, mobile databases, data replication, distributed systems, and computer and network security. Kam-Wing Chu was born in Hong Kong. He received his BS and MPhil degrees in computer science and engineering from The Chinese University of Hong Kong. When he was in Hong Kong, his research interests included database indexing of high dimensional data, and data mining. He later went to United States and received his MS degree in computer science from University of Maryland at College Park. While he was in Maryland, he focused on high performance implementation and algorithm design of advanced database systems. He is currently a senior software engineer in Server Performance group at Actuate Corporation. His expertise is in enterprise software development and software performance optimization.     

Oscillation amplitude-controlled resonant accelerometer design using a reference tracking automatic gain control

In this paper, it is presented a novel approach for the self-sustained resonant accelerometer design, which takes advantages of an automatic gain control in achieving stabilized oscillation dynamics. Through the proposed system modeling and loop transformation, the feedback controller is designed to maintain uniform oscillation amplitude under dynamic input accelerations. The fabrication process for the mechanical structure is illustrated in brief. Computer simulation and experimental results show the feasibility of the proposed accelerometer design, which is applicable to a control grade inertial sense system. Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Hyun Seok Yang. This work was supported by the BK21 Project ST·IT Fusion Engineering program in Konkuk University, 2008. This work was supported by the Korea Foundation for International Cooperation of Science & Technology(KICOS) through a grant provided by the Korean Ministry of Education, Science & Technology(MEST) in 2008 (No. K20601000001). Authors also thank to Dr. B.-L. Lee for the help in structure manufacturing. Sangkyung Sung is an Assistant Professor of the Department of Aerospace Engineering at Konkuk University, Korea. He received the M.S and Ph.D. degrees in Electrical Engineering from Seoul National University in 1998 and 2003, respectively. His research interests include inertial sensors, avionic system hardware, navigation filter, and intelligent vehicle systems. Chang-Joo Kim is an Assistant Professor of the Department of Aerospace Engineering at Konkuk University, Korea. He received the Ph.D. degree in Aeronautical Engineering from Seoul National University in 1991. His research interests include nonlinear optimal control, helicopter flight mechanics, and helicopter system design. Young Jae Lee is a Professor of the Department of Aerospace Engineering at Konkuk University, Korea. He received the Ph.D. degree in Aerospace Engineering from the University of Texas at Austin in 1990. His research interests include integrity monitoring of GNSS signal, GBAS, RTK, attitude determination, orbit determination, and GNSS related engineering problems. Jungkeun Park is an Assistant Professor of the Department of Aerospace Engineering at Konkuk University. Dr. Park received the Ph.D. in Electrical Engineering and Computer Science from the Seoul National University in 2004. His current research interests include embedded real-time systems design, real-time operating systems, distributed embedded real-time systems and multimedia systems. Joon Goo Park is an Assistant Professor of the Department of Electronic Engineering at Gyung Book National University, Korea. He received the Ph.D. degree in School of Electrical Engineering from Seoul National University in 2001. His research interests include mobile navigation and adaptive control.     

Relational semantics for locally nondeterministic programs

Introducing nondeterministic operators in a conventional deterministic language gives rise to various semantic difficulties. One of the problems is that there has been no semantic domain that is wholly satisfactory for denoting nondeterministic programs. In this paper, we propose an approach based on relational algebra. We divide the semantics of a nondeterministic program into two parts. The first part concerns the angelic aspect of programs and the second part concerns the demonic aspect of programs. Because each semantic function used in these parts is monotonic with respect to an ordering on relations, the existence of the fixed points of recursively defined nondeterministic programs is ensured. Liangwei Xu: His research interests are computational model, program transformation and derivation methodology. He received the B. E. degree from Shanghai Jiao Tong University in 1982 and the M.E. degree from University of Tokyo in 1992. He currently joins Mathematical Systems Institute Inc. Masato Takeichi, Dr. Eng.: He is a Professor of Department of Mathematical Engineering. Graduate School of Engineering, University of Tokyo. His research interests are functional programming, language implementation and constructive algorithmics. Hideya Iwasaki, Dr. Eng.: He is an Associate Professor of Faculty of Technology, Tokyo University of Agriculture and Technology. He received the M.E. degree in 1985, the Dr. Eng. degree in 1988 from University of Tokyo. His research interests are list processing languages, functional languages, parallel processing, and constructive algorithmics.     

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.     

Some impossibility results in interprocess synchronization

Summary In this paper we construct a formal specification of the problem of synchronizing asynchronous processes under strong fairness. We prove that strong interaction fairness is impossible for binary (and hence for multiway) interactions and strong process fairness is impossible for multiway interactions. Yih-Kuen Tsay received his B.S. degree form National Taiwan University in 1984 and his M.S. degree from UCLA in 1989. He is currently a Ph.D. candidate in the UCLA Computer Science Department. His research interests include distributed algorithms, fault-tolerant systems, and specification and verification of concurrent programs. Rajive L. Bagrodia received the B. Tech. degree in Electrical Engineering from the Indian Institute of Technology, Bombay in 1981 and the M.A. and Ph.D. degrees in Computer Science from the University of Texas at Austin in 1983 and 1987 respectively. He is currently an Assistant Professor in the Computer Science Department at UCLA. His research interests include parallel languages, distributed algorithms, parallel simulation and software design methodologies. He was selected as a 1991 Presidential Young Investigator by NSF.This research was partially supported by NSF PYI Award number ASC9157610 and by ONR under grant N00014-91-J1605     

Stability of nonlinear hybrid dynamical systems with time delay via sum of squares decomposition

Considering an infinite number of eigenvalues for time delay systems, it is difficult to determine their stability. We have developed a new approach for the stability test of time delay nonlinear hybrid systems. Construction of Lyapunov functions for hybrid systems is generally a difficult task, but once these functions are found, stability’s analysis of the system is straight-forward. In this paper both delay-independent and delay-dependent stability tests are proposed, based on the construction of appropriate Lyapunov-Krasovskii functionals. The methodology is based on the sum of squares decomposition of multivariate polynomials and the algorithmic construction is achieved through the use of semidefinite programming. The reduction techniques provide numerical solution of large-scale instances; otherwise they will be computationally infeasible to solve. The introduced method can be used for hybrid systems with linear or nonlinear vector fields. Finally simulation results show the correctness and validity of the designed method. Recommended by Editorial Board member Young Soo Suh under the direction of Editor Jae Weon Choi. The authors wish to express their thanks to Dr. A. Papachristodoulou and Dr. M. Peet for their helpful comments and suggestions. Mohammad Ali Badamchizadeh was born in Tabriz, Iran, in December 1975. He received the B.S. degree in Electrical Engineering from University of Tabriz in 1998 and the M.Sc. degree in Control Engineering from University of Tabriz in 2001. He received the Ph.D. degree in Control Engineering from University of Tabriz in 2007. He is now an Assistant Professor in the Faculty of Electrical and Computer Engineering at University of Tabriz. His research interests include Hybrid dynamical systems, Stability of systems, Time delay systems, Robot path planning. Sohrab Khanmohammadi received the B.S. degree in Industrial Engineering from Sharif University, Iran in 1977 and the M.Sc. degree in Automatic from University Paul Sabatie, France in 1980 and the Ph.D. degree in Automatic from National University, ENSAE, France in 1983. He is now a Professor of Electrical Engineering at University of Tabriz. His research interests are Fuzzy control, Artificial Intelligence applications in control and simulation on industrial systems and human behavior. Gasem Alizadeh was born in Tabriz, Iran in 1967. He received the B.S. degree in Electrical Engineering from Sharif University, Iran in 1990 and the M.Sc. degree from Khajeh Nasir Toosi University, Iran in 1993 and the Ph.D. degree in Electrical Engineering from Tarbiat Modarres University, Iran in 1998. From 1998, he is a Member of University of Tabriz in Iran. His research interests are robust and optimal control, guidance, navigation and adaptive control. Ali Aghagolzadeh was born in Babol, Iran. He received the B.S. degree in Electrical Engineering in 1985 from University of Tabriz, Tabriz, Iran, and the M.Sc. degree in Electrical Engineering in 1988 from the Illinois Institute of Technology, Chicago, IL. He also attended the School of Electrical Engineering at Purdue University in August 1998 where he was also employed as a part-time research assistant and received the Ph.D. degree in 1991. He is currently an Associate Professor of Electrical Engineering at University of Tabriz, Tabriz, Iran. His research interests include digital signal and image processing, image coding and communication, computer vision, and image analysis.     

Calculating accumulations

Theaccumulation strategy consists of generalizing a function over an algebraic data structure by inclusion of an extra parameter, anaccumulating parameter, for reusing and propagating intermediate results. However, there remain two major difficulties in this accumulation strategy. One is to determinewhere andwhen to generalize the original function. The other, surprisingly not yet receiving its worthy consideration, is how to manipulate accumulations. To overcome these difficulties, we propose to formulate accumulations ashigher order catamorphisms, and provide several general transformation rules for calculating accumulations (i.e., finding and manipulating accumulations) bycalculation-based (rather than a search-based) program transformation methods. Some examples are given for illustration. Zhenjiang Hu, Dr.Eng.: He is an Assistant Professor in Information Engineering at the University of Tokyo. He received his BS and MS in Computer Science from Shanghai Jiao Tong University in 1988 and 1990 respectively, and his Dr. Eng. degree in Information Engineering from the University of Tokyo in 1996. His current research concerns programming languages, functional programming, program transformation, and parallel processing. Hideya Iwasaki, Dr.Eng.: He is an Associate Professor in Information Engineering at the University of Tokyo. He received the M.E. degree in 1985, the Dr. Eng. degree in 1988 from the University of Tokyo. His research interests are list processing languages, functional languages, parallel processing, and constructive algorithmics. Masato Takeichi, Dr.Eng.: He is Professor in Mathematical Engineering and Information Engineering at the University of Tokyo since 1993. After graduation from the University of Tokyo, he joined the faculty at the University of Electro-Communications in Tokyo before he went back to work at the University of Tokyo in 1987. His research concerns the design and implementation of functional programming languages, and calculational program transformation systems.     

Polynomial-time learning of elementary formal systems

An elementary formal system (EFS) is a logic program consisting of definite clauses whose arguments have patterns instead of first-order terms. We investigate EFSs for polynomial-time PAC-learnability. A definite clause of an EFS is hereditary if every pattern in the body is a subword of a pattern in the head. With this new notion, we show that H-EFS(m, k, t, r) is polynomial-time learnable, which is the class of languages definable by EFSs consisting of at mostm hereditary definite clauses with predicate symbols of arity at mostr, wherek andt bound the number of variable occurrences in the head and the number of atoms in the body, respectively. The class defined by all finite unions of EFSs in H-EFS(m, k, t, r) is also polynomial-time learnable. We also show an interesting series ofNC-learnable classes of EFSs. As hardness results, the class of regular pattern languages is shown not polynomial-time learnable unlessRP=NP. Furthermore, the related problem of deciding whether there is a common subsequence which is consistent with given positive and negative examples is shownNP-complete. Satoru Miyano, Dr. Sci.: He is a Professor in Human Genome Center at the University of Tokyo. He obtained B.S. in 1977, M.S. in 1979, and Dr. Sci. degree all in Mathematics from Kyushu University. His current interests include bioinformatics, discovery science, computational complexity, computational learning. He has been organizing Genome Informatics Workshop Series since 1996 and has served for the chair/member of the program committee of many conferences in the area of Computer Science and Bioinformatics. He is on the Editorial Board of Theoretical Computer Science and the Chief Editor of Genome Informatics Series. Ayumi Shinohara, Dr. Sci.: He is an Associate Professor in the Department of Informatics at Kyushu University. He obtained B.S. in 1988 in Mathematics, M.S. in 1990 in Information Systems, and Dr. Sci. degree in 1994 all from Kyushu University. His current interests include discovery science, bioinformatics, and pattern matching algorithms. Takeshi Shinohara, Dr. Sci.: He is a Professor in the Department of Artificial Intelligence at Kyushu Institute of Technology. He obtained his B.S. in Mathematics from Kyoto University in 1980, and his Dr. Sci. degree from Kyushu University in 1986. His research interests are in Computational/Algorithmic Learning Theory, Information Retrieval, and Approximate Retrieval of Multimedia Data.     

Automatic outlier detection for time series: an application to sensor data

In this article we consider the problem of detecting unusual values or outliers from time series data where the process by which the data are created is difficult to model. The main consideration is the fact that data closer in time are more correlated to each other than those farther apart. We propose two variations of a method that uses the median from a neighborhood of a data point and a threshold value to compare the difference between the median and the observed data value. Both variations of the method are fast and can be used for data streams that occur in quick succession such as sensor data on an airplane. Martin Meckesheimer has been a member of the Applied Statistics Group at Phantom Works, Boeing since 2001. He received a Bachelor of Science Degree in Industrial Engineering from the University of Pittsburgh in 1997, and a Master's Degree in Industrial and Systems Engineering from Ecole Centrale Paris in 1999. Martin earned a Doctorate in Industrial Engineering from The Pennsylvania State University in August 2001, as a student of Professor Russell R. Barton and Dr. Timothy W. Simpson. His primary research interests are in the areas of design of experiments and surrogate modeling. Sabyasachi Basu received his Ph.D. is Statistics from the University of Wisconsin at Madison in 1990. Since his Ph.D., he has worked in both academia and in industry. He has taught and guided Ph.D. students in the Department of Statistics at the Southern Methodist University. He has also worked as a senior marketing statistician at the J. C. Penney Company. Dr. Basu is also an American Society of Quality certified Six Sigma Black Belt. He is currently an Associate Technical Fellow in Statistics and Data Mining at the Boeing Company. In this capacity, he works as a researcher and a technical consultant within Boeing for data mining, statistics and process improvements. He has published more than 20 papers and technical reports. He has also served as journal referee for several journals, organized conferences and been invited to present at conferences.     

Discovering characteristic patterns from collections of classical Japanese poems

Waka is a form of traditional Japanese poetry with a 1300-year history. In this paper, we attempt to discover characteristics common to a collection ofwaka poems. As a schema for characteristics, we use regular patterns where the constant parts are limited to sequences of auxiliary verbs and postpostional particles. We call such patternsfushi. The problem is to automate the process of finding significantfushi patterns that characterize the poems. Solving this problem requires a reliable significance measure for the patterns. Brāzma et al. (1996) proposed such a measure according to the MDL principle. Using this method, we report successful results in finding patterns from five anthologies. Some of the results are quite stimulating, and we hope that they will lead to new discoveries. Mayumi Yamasaki, M.A.: She received her B.E. and M.A. degrees from Kyushu Institute of Technology in 1997 and from Kyushu University in 1999, respectively. Her research interests include machine discovery and datamining. Presently, she works at Fujitsu FIP Corporation. Masayuki Takeda, Dr. Eng.: He is an Associate Professor in Department of informatics at Kyushu University. He received his B.S., M.S., and Dr. Eng. degrees from Kyushu University in 1987, 1989 and 1996 respectively. His present research interests include pattern matching algorithims, text compression, discovery science, information retrieval and natural language processing. He is a member of Information Processing Society of Japan, Japanese Society for Artificial Intelligence and Japanese Society for Soft-ware Science and Technology. Tomoko Fukuda, M.A.: She is a Lecturer at Fukuoka Jo Gakuin University and at Junshin Women’s Junior College, She received her B.A. and M.A. degrees from Fukuoka Women’s University in 1987 and from Kyushu University in 1992 respectively. Her present research interests are in Japanese literature in the Heian period and classical 31-syllable Japanese poems. She is a member of Waka-Bungaku Kai (Society for Study of Japanese Poems) and Chuko-Bungaku Kai (Society for Study of Japanese Literature in the Heian Period). Ichiro Nanri, M.A.: He is an Associate Professor at Junshin Women’s Juior College. He received his B.A. and M.A. degrees from Kyushu University in 1990 and 1995 respectively. His present research interests are in Japanese language in the Heian-Kamakura period and classical 31-syllable Japanese poems. He is a member of Kokugo Gakkai (Society for Study of Japanese Language) and Kuntengo Gakkai (Society for Studyy of Old Language).     

Singular value decomposition based data distortion strategy for privacy protection

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

Efficient discovery of optimal word-association patterns in large text databases

We study efficient discovery of proximity word-association patterns, defined by a sequence of strings and a proximity gap, from a collection of texts with the positive and the negative labels. We present an algorithm that finds alld-stringsk-proximity word-association patterns that maximize the number of texts whose matching agree with their labels. It runs in expected time complexityO(k ^d−1n log ^d n) and spaceO(k ^d−1n) with the total lengthn of texts, if texts are uniformly random strings. We also show that the problem to find one of the best word-association patterns with arbitrarily many strings in MAX SNP-hard. Shinichi Shimozono, Ph.D.: He is an Associate Professor of the Department of Artificial Intelligence at Kyushu Institute of Technology Iizuka, Japan. He obtained the B.S. degree in Physics from Kyushu University, awarded M.S. degree from Graduate School of Information Science in Kyushu University, and his Dr. Sci. degree in 1996 from Kyushu University. His research interests are primarily in the design and analysis of algorithms for intractable problems. Hiroki Arimura, Ph.D.: He is an Associate Professor of the Department of Informatics at Kyushu University, Fukuoka, Japan. He is also a researcher with Precursory Research for Embryonic Science and Technology, Japan Science and Technology Corporation (JST) since 1999. He received the B.S. degree in 1988 in Physics, the M.S. degree in 1979 and the Dr.Sci. degree in 1994 in Information Systems from Kyushu University. His research interests include data mining, computational learning theory, and inductive logic programming. Setsuo Arikawa, Ph.D.: He is a Professor of the Department of Informatics and the Director of University Library at Kyushu University, Fukuoka, Japan. He received the B.S. degree in 1964, the M.S. degree in 1966 and the Dr.Sci. degree in 1969 all in Mathematics from Kyushu University. His research interests include Discovery Science, Algorithmic Learning Theory, Logic and Inference/Reasoning in AI, Pattern Matching Algorithms and Library Science. He is the principal investigator of the Discovery Science Project sponsored by the Grant-in Aid for Scientific Research on Priority Area from the Ministry of ESSC, Japan.     

Adaptive solutions to the mutual exclusion problem

Summary Algorithms for mutual exclusion that adapt to the current degree of contention are developed. Afilter and a leader election algorithm form the basic building blocks. The algorithms achieve system response times that are independent of the total number of processes and governed instead by the current degree of contention. The final algorithm achieves a constant amortized system response time. Manhoi Choy was born in 1967 in Hong Kong. He received his B.Sc. in Electrical and Electronic Engineerings from the University of Hong Kong in 1989, and his M.Sc. in Computer Science from the University of California at Santa Barbara in 1991. Currently, he is working on his Ph.D. in Computer Science at the University of California at Santa Barbara. His research interests are in the areas of parallel and distributed systems, and distributed algorithms. Ambuj K. Singh is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara. He received a Ph.D. in Computer Science from the University of Texas at Austin in 1989, an M.S. in Computer Science from Iowa State University in 1984, and a B.Tech. from the Indian Institute of Technology at Kharagpur in 1982. His research interests are in the areas of adaptive resource allocation, concurrent program development, and distributed shared memory.A preliminary version of the paper appeared in the 12th Annual ACM Symposium on Principles of Distributed ComputingWork supported in part by NSF grants CCR-9008628 and CCR-9223094     

Error Analysis for Image Inpainting

Image inpainting refers to restoring a damaged image with missing information. In recent years, there have been many developments on computational approaches to image inpainting problem [2, 4, 6, 9, 11–13, 27, 28]. While there are many effective algorithms available, there is still a lack of theoretical understanding on under what conditions these algorithms work well. In this paper, we take a step in this direction. We investigate an error bound for inpainting methods, by considering different image spaces such as smooth images, piecewise constant images and a particular kind of piecewise continuous images. Numerical results are presented to validate the theoretical error bounds. Tony F. Chan received the B.S. degree in engineering and the M.S. degree in aerospace engineering in 1973, from the California Institute of Technology, and the Ph.D. degree in computer science from Stanford University in 1978. He is Professor of Mathematics and currently also Dean of the division of Physical science at University of California, Los Angeles, where he has been a Professor since 1986. His research interests include mathematical and computational methods in image processing, multigrid, domain decomposition algorithms, iterative methods, Krylov subspace methods, and parallel algorithms. Sung Ha Kang received the Ph.D. degree in mathematics in 2002, from University of California, Los Angeles, and currently is Assistant Professor of Mathematics at University of Kentucky since 2002. Her research interests include mathematical and computational methods in image processing and computer vision.     

Using genetic algorithms to reorganize superpeer structure in peer to peer networks

In this article, we describe a genetic algorithm for optimizing the superpeer structure of semantic peer to peer networks. Peer to peer, also called P2P, networks enable us to search for content or information in a distributed fashion across a large number of peers while providing a level of fault tolerance by preventing disconnecting peers from disrupting the network. We seek to maximize the number of queries answered while minimizing the time in which they are answered. It will be shown that the genetic algorithm (GA) dramatically improves network performance and consistently finds networks better than those found by random search and hill climbing. A comparison will also be made to networks found through exhaustive search, showing that the GA will, for smaller networks, converge on a globally optimal solution. Jaymin Kessler has a bachelors degree in Computer Science from Ramapo College and a Masters Degree in AI from the University of Georgia. After graduation, he worked for Hypnotix making PS2 and Xbox games, and is currently working as a software engineer at Electronic Arts Tiburon. Khaled Rasheed is an Associate Professor of Computer Science and the graduate coordinator of the Artificial Intelligence Center at the University of Georgia. His research interests include evolutionary computation, machine learning and bioinformatics. Dr. Rasheed received his PhD in computer science from Rutgers University in New Jersey. I. Budak Arpinar is an Assistant Professor of Computer Science and member of the Large Scale Distributed Information Systems Lab at the University of Georgia. His research interests include semantic web, web services, and peer-to-peer systems. Dr. Arpinar received his PhD in computer science from the Middle East Technical University in Turkey.