共查询到20条相似文献,搜索用时 46 毫秒
1.
Manish Gupta Manghui Tu Latifur Khan Farokh Bastani I-Ling Yen 《Knowledge and Information Systems》2005,8(4):414-437
Advances in wireless and mobile computing environments allow a mobile user to access a wide range of applications. For example,
mobile users may want to retrieve data about unfamiliar places or local life styles related to their location. These queries
are called location-dependent queries. Furthermore, a mobile user may be interested in getting the query results repeatedly,
which is called location-dependent continuous querying. This continuous query emanating from a mobile user may retrieve information
from a single-zone (single-ZQ) or from multiple neighbouring zones (multiple-ZQ). We consider the problem of handling location-dependent
continuous queries with the main emphasis on reducing communication costs and making sure that the user gets correct current-query
result. The key contributions of this paper include: (1) Proposing a hierarchical database framework (tree architecture and
supporting continuous query algorithm) for handling location-dependent continuous queries. (2) Analysing the flexibility of
this framework for handling queries related to single-ZQ or multiple-ZQ and propose intelligent selective placement of location-dependent
databases. (3) Proposing an intelligent selective replication algorithm to facilitate time- and space-efficient processing
of location-dependent continuous queries retrieving single-ZQ information. (4) Demonstrating, using simulation, the significance
of our intelligent selective placement and selective replication model in terms of communication cost and storage constraints,
considering various types of queries.
Manish Gupta received his B.E. degree in Electrical Engineering from Govindram Sakseria Institute of Technology & Sciences, India, in
1997 and his M.S. degree in Computer Science from University of Texas at Dallas in 2002. He is currently working toward his
Ph.D. degree in the Department of Computer Science at University of Texas at Dallas. His current research focuses on AI-based
software synthesis and testing. His other research interests include mobile computing, aspect-oriented programming and model
checking.
Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China, in 1996, and a Master's Degree in Computer Science
from the University of Texas at Dallas 2001. He is currently working toward the Ph.D. degree in the Department of Computer
Science at the University of Texas at Dallas. Mr. Tu's research interests include distributed systems, wireless communications,
mobile computing, and reliability and performance analysis. His Ph.D. research work focuses on the dependent and secure data
replication and placement issues in network-centric systems.
Latifur R. Khan has been an Assistant Professor of Computer Science department at University of Texas at Dallas since September 2000. He
received his Ph.D. and M.S. degrees in Computer Science from University of Southern California (USC) in August 2000 and December
1996, respectively. He obtained his B.Sc. degree in Computer Science and Engineering from Bangladesh University of Engineering
and Technology, Dhaka, Bangladesh, in November of 1993. Professor Khan is currently supported by grants from the National
Science Foundation (NSF), Texas Instruments, Alcatel, USA, and has been awarded the Sun Equipment Grant. Dr. Khan has more
than 50 articles, book chapters and conference papers focusing in the areas of database systems, multimedia information management
and data mining in bio-informatics and intrusion detection. Professor Khan has also served as a referee for database journals,
conferences (e.g. IEEE TKDE, KAIS, ADL, VLDB) and he is currently serving as a program committee member for the 11th ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining (SIGKDD2005), ACM 14th Conference on Information and Knowledge
Management (CIKM 2005), International Conference on Database and Expert Systems Applications DEXA 2005 and International Conference
on Cooperative Information Systems (CoopIS 2005), and is program chair of ACM SIGKDD International Workshop on Multimedia
Data Mining, 2004.
Farokh Bastani received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology, Bombay, and the M.S. and Ph.D.
degrees in Computer Science from the University of California, Berkeley. He is currently a Professor of Computer Science at
the University of Texas at Dallas. Dr. Bastani's research interests include various aspects of the ultrahigh dependable systems,
especially automated software synthesis and testing, embedded real-time process-control and telecommunications systems and
high-assurance systems engineering.
Dr. Bastani was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (IEEE-TKDE). He is currently
an emeritus EIC of IEEE-TKDE and is on the editorial board of the International Journal of Artificial Intelligence Tools,
the International Journal of Knowledge and Information Systems and the Springer-Verlag series on Knowledge and Information
Management. He was the program cochair of the 1997 IEEE Symposium on Reliable Distributed Systems, 1998 IEEE International
Symposium on Software Reliability Engineering, 1999 IEEE Knowledge and Data Engineering Workshop, 1999 International Symposium
on Autonomous Decentralised Systems, and the program chair of the 1995 IEEE International Conference on Tools with Artificial
Intelligence. He has been on the program and steering committees of several conferences and workshops and on the editorial
boards of the IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering and the Oxford
University Press High Integrity Systems Journal.
I-Ling Yen received her B.S. degree from Tsing-Hua University, Taiwan, and her M.S. and Ph.D. degrees in Computer Science from the University
of Houston. She is currently an Associate Professor of Computer Science at University of Texas at Dallas. Dr. Yen's research
interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet technologies, E-commerce
and self-stabilising systems. She has published over 100 technical papers in these research areas and received many research
awards from NSF, DOD, NASA and several industry companies. She has served as Program Committee member for many conferences
and Program Chair/Cochair for the IEEE Symposium on Application-Specific Software and System Engineering & Technology, IEEE
High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications Conference, and IEEE International
Symposium on Autonomous Decentralized Systems. She has also served as a guest editor for a theme issue of IEEE Computer devoted
to high-assurance systems. 相似文献
2.
It is likely that customers issue requests based on out-of-date information in e-commerce application systems. Hence, the
transaction failure rates would increase greatly. In this paper, we present a preference update model to address this problem.
A preference update is an extended SQL update statement where a user can request the desired number of target data items by
specifying multiple preferences. Moreover, the preference update allows easy extraction of criteria from a set of concurrent
requests and, hence, optimal decisions for the data assignments can be made. We propose a group evaluation strategy for preference
update processing in a multidatabase environment. The experimental results show that the group evaluation can effectively
increase the customer satisfaction level with acceptable cost.
Peng Li is the Chief Software Architect of didiom LLC. Before that, he was a visiting assistant professor of computer science department
in Western Kentucky University. He received his Ph.D. degree of computer science from the University of Texas at Dallas. He
also holds a B.Sc. and M.S. in Computer Science from the Renmin University of China. His research interests include database
systems, database security, transaction processing, distributed and Internet computer and E-commerce.
Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China in 1996, and a Master Degree in Computer Science
from the University of Texas at Dallas 2001. He is currently working toward the PhD degree in the Department of Computer Science
at the University of Texas at Dallas. Mr. Tu’s research interests include distributed systems, grid computing, information
security, mobile computing, and scientific computing.
His PhD research work focus on the data management in secure and high performance data grid. He is a student member of the
IEEE.
I-Ling Yen received her BS degree from Tsing-Hua University, Taiwan, and her MS and PhD degrees in Computer Science from the University
of Houston. She is currently an Associate Professor of Computer Science at the University of Texas at Dallas.
Dr. Yen’s research interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet
technologies, E-commerce, and self-stabilizing systems. She had published over 100 technical papers in these research areas
and received many research awards from NSF, DOD, NASA, and several industry companies. She has served as Program Committee
member for many conferences and Program Chair/Co-Chair for the IEEE Symposium on Application-Specific Software and System
Engineering & Technology, IEEE High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications
Conference, and IEEE International Symposium on Autonomous Decentralized Systems. She is a member of the IEEE.
Zhonghang Xia received the B.S. degree in applied mathematics from Dalian University of Technology in 1990, the M.S. degree in Operations
Research from Qufu Normal University in 1993, and the Ph.D. degree in computer science from the University of Texas at Dallas
in 2004. He is now an assistant professor in the Department of Computer Science, Western Kentucky University, Bowling Green,
KY. His research interests are in the area of multimedia computing and networking, distributed systems, and data mining. 相似文献
3.
The pairwise attribute noise detection algorithm 总被引:1,自引:3,他引:1
Jason D. Van Hulse Taghi M. Khoshgoftaar Haiying Huang 《Knowledge and Information Systems》2007,11(2):171-190
Analyzing the quality of data prior to constructing data mining models is emerging as an important issue. Algorithms for identifying
noise in a given data set can provide a good measure of data quality. Considerable attention has been devoted to detecting
class noise or labeling errors. In contrast, limited research work has been devoted to detecting instances with attribute
noise, in part due to the difficulty of the problem. We present a novel approach for detecting instances with attribute noise
and demonstrate its usefulness with case studies using two different real-world software measurement data sets. Our approach,
called Pairwise Attribute Noise Detection Algorithm (PANDA), is compared with a nearest neighbor, distance-based outlier detection
technique (denoted DM) investigated in related literature. Since what constitutes noise is domain specific, our case studies
uses a software engineering expert to inspect the instances identified by the two approaches to determine whether they actually
contain noise. It is shown that PANDA provides better noise detection performance than the DM algorithm.
Jason Van Hulse is a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests
include data mining and knowledge discovery, machine learning, computational intelligence and statistics. He is a student
member of the IEEE and IEEE Computer Society. He received the M.A. degree in mathematics from Stony Brook University in 2000,
and is currently Director, Decision Science at First Data Corporation.
Taghi M. Khoshgoftaar is a professor at the Department of Computer Science and Engineering, Florida Atlantic University, and the director of the
Empirical Software Engineering and Data Mining and Machine Learning Laboratories. His research interests are in software engineering,
software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation,
data mining, machine learning, and statistical modeling. He has published more than 300 refereed papers in these subjects.
He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring
agencies. He is a member of the IEEE, the IEEE Computer Society, and IEEE Reliability Society. He served as the program chair
and general chair of the IEEE International Conference on Tools with Artificial Intelligence in 2004 and 2005, respectively.
Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has
served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems.
Haiying Huang received the M.S. degree in computer engineeringfrom Florida Atlantic University, Boca Raton, Florida, USA, in 2002. She
is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. Her research
interests include software engineering, computational intelligence, data mining, software measurement, software reliability,
and quality engineering. 相似文献
4.
The novel idea of setting up Internet-based virtual markets, information markets, to aggregate dispersed information and predict
outcomes of uncertain future events has empirically found its way into many domains. But the theoretical examination of information
markets has lagged relative to their implementation and use. This paper proposes a simple theoretical model of information
markets to understand their information dynamics. We investigate and provide initial answers to a series of research questions
that are important to understanding how information markets work, which are: (1) Does an information market converge to a
consensus equilibrium? (2) If yes, how fast is the convergence process? (3) What is the best possible equilibrium of an information
market? and (4) Is an information market guaranteed to converge to the best possible equilibrium?
The authors acknowledge the support of the eBusiness Research Center at the Pennsylvania State University.
Yiling Chen is a postdoctoral research scientist at Yahoo! Research, New York. She received her Bachelor of Economics degree in Commodity
Science from Renmin University of China, in 1996, and her Master of Economics degree in Finance from Tsinghua University,
China, in 1999. She worked for PriceWaterhouse Coopers China as a professional auditor from August 1999 to June 2000. From
August 2000 to July 2001, she attended Iowa State University, Ames, IA, as a Ph.D. student in economics. She obtained her
Ph.D. in Information Sciences and Technology from the Pennsylvania State University, University Park, PA, in 2005. Her research
interests lie on the boarder of computer science, economics, and business, including information markets, auction theory,
and machine learning.
Tracy Mullen is an assistant professor of information sciences and technology at the Pennsylvania State University, University Park, PA.
She has previously worked at Lockheed Martin, Bellcore, and NEC Research. She received her PhD in Computer Science from University
of Michigan. Her research interests include information markets, multiagent systems, ecommerce, market-based resource allocation
for sensor management, and supply chain simulations using intelligent agents. Her research papers have been published in Decision
Support Systems, Electronic Commerce Research, IEEE Computer, ACM Transactions on Internet Technology, Mathematics and Computers
in Simulation, and Operating Systems Review, among others.
Chao-Hsien Chu is an associate professor of information sciences and technology and the executive director of the Center for Information
Assurance at the Pennsylvania State University, University Park, PA. He was previously on the faculty at Iowa State University,
Iowa and Baruch College, New York and a visiting professor at University of Tsukuba (Japan) and Hebei University of Technology
(China). He is currently on leaves to the Singapore Management University (Singapore) (2005–2006). Dr. Chu received a Ph.D.
in Business Administration from Penn State. His current research interests are in communication networks design, information
assurance and security (especially in wireless security, intrusion detection, and cyber forensics), intelligent technologies
(fuzzy logic, neural network, genetic algorithms, etc.) for data mining (e.g., bioinformatics, privacy preserving) and supply
chains integration. His research papers have been published in Decision Sciences, IEEE Transactions on Evolutionary Computation,
IIE Transactions, Decision Support Systems, European Journal of Operational Research, Electronic Commerce Research, Expert
Systems with Applications, International Journal of Mobile Communications, Journal of Operations Management, International
Journal of Production Research, among others. He is currently on the editorial review board for a number of journals. 相似文献
5.
6.
Paola Mello Michela Milano Marco Gavanelli Evelina Lamma Massimo Piccardi Rita Cucchiara 《New Generation Computing》2001,19(4):339-367
*1 Constraint Satisfaction Problems (CSPs)17) are an effective framework for modeling a variety of real life applications and many techniques have been proposed for solving
them efficiently. CSPs are based on the assumption that all constrained data (values in variable domains) are available at
the beginning of the computation. However, many non-toy problems derive their parameters from an external environment. Data
retrieval can be a hard task, because data can come from a third-party system that has to convert information encoded with
signals (derived from sensors) into symbolic information (exploitable by a CSP solver). Also, data can be provided by the
user or have to be queried to a database.
For this purpose, we introduce an extension of the widely used CSP model, called Interactive Constraint Satisfaction Problem
(ICSP) model. The variable domain values can be acquired when needed during the resolution process by means of Interactive
Constraints, which retrieve (possibly consistent) information. A general framework for constraint propagation algorithms is
proposed which is parametric in the number of acquisitions performed at each step. Experimental results show the effectiveness
of the proposed approach. Some applications which can benefit from the proposed solution are also discussed.
This paper is an extended and revised version of the paper presented at IJCAI’99 (Stockholm, August 1999)4).
Paola Mello, Ph.D.: She received her degree in Electronic Engineering from University of Bologna, Italy, in 1982 and her Ph.D. degree in Computer
Science in 1989. Since 1994 she is full Professor. She is enrolled, at present, at the Faculty of Engineering of the University
of Bologna where she teaches Artificial Intelligence. Her research activity focuses around: programming languages, with particular
reference to logic languages and their extensions towards modular and object-oriented programming; artificial intelligence;
knowledge representation; expert systems. Her research has covered implementation, application and theoretical aspects and
is presented in several national and international publications. She took part to several national (Progetti Finalizzati e
MURST) and international (UE) research projects in the context of computational logic.
Michela Milano, Ph.D.: She is a Researcher in the Department of Electronics, Computer Science and Systems at the University of Bologna. From the
same University she obtained her master degree in 1994 and her Ph.D. in 1998. In 1999 she had a post-doc position at the University
of Ferrara. Her research focuses on Artificial Intelligence, Constraint Satisfaction and Constraint Programming. In particular,
she worked on using and extending the constraint-based paradigm for solving real-life problems such as scheduling, routing,
object recognition and planning. She has served on the program committees of several international conferences in the area
of Constraint Satisfaction and Programming, and she has served as referee in several related international journals.
Marco Gavanelli: He is currently a Ph.D. Student in the Department of Engineering at the University of Ferrara, Italy. He graduated in Computer
Science Engineering in 1998 at the University of Bologna, Italy. His research interest include Artificial Intelligence, Constraint
Logic Programming, Constraint Satisfaction and visual recognition. He is a member of ALP (the Association for Logic Programming)
and AI*IA (the Italian Association for Artificial Intelligence).
Evelina Lamma, Ph.D.: She got her degree in Electrical Engineering at the University of Bologna in 1985, and her Ph.D. in Computer Science in 1990.
Her research activity centers on logic programming languages, Artificial Intelligence and software engineering. She was co-organizers
of the 3rd International Workshop on Extensions of Logic Programming ELP92, held in Bologna in February 1992, and of the 6th
Italian Congress on Artificial Intelligence, held in Bologna in September 1999. She is a member of the Executive Committee
of the Italian Association for Artificial Intelligence (AI*IA). Currently, she is Full Professor at the University of Ferrara, where she teaches Artificial Intelligence and Fondations
of Computer Science.
Massimo Piccardi, Ph.D.: He graduated in electronic engineering at the University of Bologna, Italy, in 1991, where he received a Ph.D. in computer
science and computer engineering in 1995. He currently an assistant professor of computer science with the Faculty of Engineering
at the University of Ferrara, Italy, where he teaches courses on computer architecture and microprocessor systems. Massimo
Piccardi participated in several research projects in the area of computer vision and pattern recognition. His research interests
include architectures, algorithms and benchmarks for computer vision and pattern recognition. He is author of more than forty
papers on international scientific journals and conference proceedings. Dr. Piccardi is a member of the IEEE, the IEEE Computer
Society, and the International Association for Pattern Recognition — Italian Chapter.
Rita Cucchiara, Ph.D.: She is an associate professor of computer science at the Faculty of Engineering at the University of Modena and Reggio Emilia,
Italy, where she teaches courses on computer architecture and computer vision. She graduated in electronic engineering at
the University of Bologna, Italy, in 1989 and she received a Ph.D. in electronic engineering and computer science from the
same university in 1993. From 1993 to 1998 she been an assistant professor of computer science with the University of Ferrara,
Italy. She participated in many research projects, including a SIMD parallel system for vision in the context of an Italian
advanced research program in robotics, funded by CNR (the Italian National Research Council). Her research interests include
architecture and algorithms for computer vision and multimedia systems. She is author of several papers on scientific journals
and conference proceedings. She is member of the IEEE, the IEEE Computer Society, and the International Association for Pattern
Recognition — Italian Chapter. 相似文献
7.
An Integrated Framework for Semantic Annotation and Adaptation 总被引:1,自引:1,他引:0
Tools for the interpretation of significant events from video and video clip adaptation can effectively support automatic extraction and distribution of relevant content from video streams. In fact, adaptation can adjust meaningful content, previously detected and extracted, to the user/client capabilities and requirements. The integration of these two functions is increasingly important, due to the growing demand of multimedia data from remote clients with limited resources (PDAs, HCCs, Smart phones). In this paper we propose an unified framework for event-based and object-based semantic extraction from video and semantic on-line adaptation. Two cases of application, highlight detection and recognition from soccer videos and people behavior detection in domotic* applications, are analyzed and discussed.Domotics is a neologism coming from the Latin word domus (home) and informatics.Marco Bertini has a research grant and carries out his research activity at the Department of Systems and Informatics at the University of Florence, Italy. He received a M.S. in electronic engineering from the University of Florence in 1999, and Ph.D. in 2004. His main research interest is content-based indexing and retrieval of videos. He is author of more than 25 papers in international conference proceedings and journals, and is a reviewer for international journals on multimedia and pattern recognition.Rita Cucchiara (Laurea Ingegneria Elettronica, 1989; Ph.D. in Computer Engineering, University of Bologna, Italy 1993). She is currently Full Professor in Computer Engineering at the University of Modena and Reggio Emilia (Italy). She was formerly Assistant Professor (‘93–‘98) at the University of Ferrara, Italy and Associate Professor (‘98–‘04) at the University of Modena and Reggio Emilia, Italy. She is currently in the Faculty staff of Computer Engenering where has in charges the courses of Computer Architectures and Computer Vision.Her current interests include pattern recognition, video analysis and computer vision for video surveillance, domotics, medical imaging, and computer architecture for managing image and multimedia data.Rita Cucchiara is author and co-author of more than 100 papers in international journals, and conference proceedings. She currently serves as reviewer for many international journals in computer vision and computer architecture (e.g. IEEE Trans. on PAMI, IEEE Trans. on Circuit and Systems, Trans. on SMC, Trans. on Vehicular Technology, Trans. on Medical Imaging, Image and Vision Computing, Journal of System architecture, IEEE Concurrency). She participated at scientific committees of the outstanding international conferences in computer vision and multimedia (CVPR, ICME, ICPR, ...) and symposia and organized special tracks in computer architecture for vision and image processing for traffic control. She is in the editorial board of Multimedia Tools and Applications journal. She is member of GIRPR (Italian chapter of Int. Assoc. of Pattern Recognition), AixIA (Ital. Assoc. Of Artificial Intelligence), ACM and IEEE Computer Society.Alberto Del Bimbo is Full Professor of Computer Engineering at the Università di Firenze, Italy. Since 1998 he is the Director of the Master in Multimedia of the Università di Firenze. At the present time, he is Deputy Rector of the Università di Firenze, in charge of Research and Innovation Transfer. His scientific interests are Pattern Recognition, Image Databases, Multimedia and Human Computer Interaction. Prof. Del Bimbo is the author of over 170 publications in the most distinguished international journals and conference proceedings. He is the author of the “Visual Information Retrieval” monography on content-based retrieval from image and video databases edited by Morgan Kaufman. He is Member of IEEE (Institute of Electrical and Electronic Engineers) and Fellow of IAPR (International Association for Pattern Recognition). He is presently Associate Editor of Pattern Recognition, Journal of Visual Languages and Computing, Multimedia Tools and Applications Journal, Pattern Analysis and Applications, IEEE Transactions on Multimedia, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He was the Guest Editor of several special issues on Image databases in highly respected journals.Andrea Prati (Laurea in Computer Engineering, 1998; PhD in Computer Engineering, University of Modena and Reggio Emilia, 2002). He is currently an assistant professor at the University of Modena and Reggio Emilia (Italy), Faculty of Engineering, Dipartimento di Scienze e Metodi dell’Ingegneria, Reggio Emilia. During last year of his PhD studies, he has spent six months as visiting scholar at the Computer Vision and Robotics Research (CVRR) lab at University of California, San Diego (UCSD), USA, working on a research project for traffic monitoring and management through computer vision. His research interests are mainly on motion detection and analysis, shadow removal techniques, video transcoding and analysis, computer architecture for multimedia and high performance video servers, video-surveillance and domotics. He is author of more than 60 papers in international and national conference proceedings and leading journals and he serves as reviewer for many international journals in computer vision and computer architecture. He is a member of IEEE, ACM and IAPR. 相似文献
8.
9.
Mining user access patterns with traversal constraint for predicting web page requests 总被引:4,自引:4,他引:0
Mei-Ling Shyu Choochart Haruechaiyasak Shu-Ching Chen 《Knowledge and Information Systems》2006,10(4):515-528
The recent increase in HyperText Transfer Protocol (HTTP) traffic on the World Wide Web (WWW) has generated an enormous amount of log records on Web server databases. Applying Web mining techniques on these server log records can discover potentially useful patterns and reveal user access behaviors on the Web site. In this paper, we propose a new approach for mining user access patterns for predicting Web page requests, which consists of two steps. First, the Minimum Reaching Distance (MRD) algorithm is applied to find the distances between the Web pages. Second, the association rule mining technique is applied to form a set of predictive rules, and the MRD information is used to prune the results from the association rule mining process. Experimental results from a real Web data set show that our approach improved the performance over the existing Markov-model approach in precision, recall, and the reduction of user browsing time.
Mei-Ling Shyu received her Ph.D. degree from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN in 1999, and three Master's degrees from Computer Science, Electrical Engineering, and Restaurant, Hotel, Institutional, and Tourism Management from Purdue University. She has been an Associate Professor in the Department of Electrical and Computer Engineering (ECE) at the University of Miami (UM), Coral Gables, FL, since June 2005, Prior to that, she was an Assistant Professor in ECE at UM dating from January 2000. Her research interests include data mining, multimedia database systems, multimedia networking, database systems, and security. She has authored and co-authored more than 120 technical papers published in various prestigious journals, refereed conference/symposium/workshop proceedings, and book chapters. She is/was the guest editor of several journal special issues.
Choochart Haruechaiyasak received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of Miami, in 2003 with the Outstanding Departmental Graduating Student award from the College of Engineering. After receiving his degree, he has joined the National Electronics and Computer Technology Center (NECTEC), located in Thailand Science Park, as a researcher in Information Research and Development Division (RDI). His current research interests include data/ text/ Web mining, Natural Language Processing, Information Retrieval, Search Engines, and Recommender Systems. He is currently leading a small group of researchers and programmer to develop an open-source search engine for Thai language. One of his objectives is to promote the use of data mining technology and other advanced applications in Information Technology in Thailand. He is also a visiting lecturer for Data Mining, Artificial Intelligence and Decision Support Systems courses in many universities in Thailand.
Shu-Ching Chen received his Ph.D. from the School of Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA in December, 1998. He also received Master's degrees in Computer Science, Electrical Engineering, and Civil Engineering from Purdue University. He has been an Associate Professor in the School of Computing and Information Sciences (SCIS), Florida International University (FIU) since August, 2004. Prior to that, he was an Assistant Professor in SCIS at FIU dating from August, 1999. His main research interests include distributed multimedia database systems and multimedia data mining. Dr. Chen has authored and co-authored more than 140 research papers in journals, refereed conference/symposium/workshop proceedings, and book chapters. In 2005, he was awarded the IEEE Systems, Man, and Cybernetics Society's Outstanding Contribution Award. He was also awarded a University Outstanding Faculty Research Award from FIU in 2004, Outstanding Faculty Service Award from SCIS in 2004 and Outstanding Faculty Research Award from SCIS in 2002. 相似文献
10.
Qianhui Althea Liang Jen-Yao Chung Steven Miller 《Knowledge and Information Systems》2007,13(3):367-394
When meeting the challenges in automatic and semi-automatic Web service composition, capturing the user’s service demand and
preferences is as important as knowing what the services can do. This paper discusses the idea of semantic service requests
for composite services, and presents a multi-attribute utility theory (MAUT) based model of composite service requests. Service
requests are modeled as user preferences and constraints. Two preference structures, additive independence and generalized
additive independence, are utilized in calculating the expected utilities of service composition outcomes. The model is also
based on an iterative and incremental scheme meant to better capture requirements in accordance with service consumers’ needs.
OWL-S markup vocabularies and associated inference mechanism are used as a means to bring semantics to service requests. Ontology
conceptualizations and language constructs are added to OWL-S as uniform representations of possible aspects of the requests.
This model of semantics in service requests enables unambiguous understanding of the service needs and more precise generation
of the desired compositions. An application scenario is presented to illustrate how the proposed model can be applied in the
real business world.
Qianhui Althea Liang received her Ph.D from the Department of Electrical and Computer Engineering, University of Florida in 2004. While pursuing
her Ph.D, she was a member of Database Systems Research and Development Center at the University of Florida. She received
both her bachelor’s and master’s from the Department of Computer Science and Engineering, Zhejiang University, China. She
joined the School of Information Systems at Singapore Management University, Singapore, as an assistant professor in 2005.
Her major research interests are service composition, dynamic service discovery, multimedia Web services, and applied artificial
intelligence.
Jen-Yao Chung received the M.S. and Ph.D degrees in computer science from the University of Illinois at Urbana-Champaign. Currently, he
is the senior manager for Engineering and Technology Services Innovation, where he was responsible for identifying and creating
emergent solutions. He was Chief Technology Officer for IBM Global Electronics Industry. Before that, he was program director
for IBM Institute for Advanced Commerce Technology office. He is the co-founder of IEEE technical committee on e-Commerce
(TCEC). He has served as general chair and program chair for many international conferences, most recently he served as the
steering committee chair for the IEEE International Conference on e-Commerce Technology (CEC06) and general chair for the
IEEE International Conference on e-Business Engineering (ICEBE06). He has authored or coauthored over 150 technical papers
in published journals or conference proceedings. He is a senior member of the IEEE and a member of ACM.
Miller is founding Dean of the School of Information Systems (SIS) at Singapore Management University, and also serves as Practice
Professor of Information Systems. Since 2003, he has led efforts to launch and establish the undergraduate, graduate and professional
programs of the SIS. Immediately prior to joining SMU, Dr. Miller served as Chief Architect Executive for the Business Consulting
Services unit of IBM Global Services in Asia Pacific. He held prior industry appointments with Fujitsu Network Systems, and
with RWD Technologies. Dr. Miller started his professional career as an Assistant Professor at Carnegie Mellon University,
conducting research and teaching related to Computer-Integrated Manufacturing and Robotics applications and impacts. He has
a Bachelors of Engineering Degree in Systems Engineering (Magna Cum Laude) from the University of Pennsylvania and a Masters
of Science in Statistics and a Ph.D in Engineering and Public Policy from Carnegie Mellon University. 相似文献
11.
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. 相似文献
12.
This paper investigates the interactions between agents representing grid users and the providers of grid resources to maximize
the aggregate utilities of all grid users in computational grid. It proposes a price-based resource allocation model to achieve
maximized utility of grid users and providers in computational grid. Existing distributed resource allocation schemes assume
the resource provider to be capable of measuring user’s resource demand, calculating and communicating price, none of which
actually exists in reality. This paper addresses these challenges as follows. First, the grid user utility is defined as a
function of the grid user’s the resource units allocated. We formalize resource allocation using nonlinear optimization theory,
which incorporates both grid resource capacity constraint and the job complete times. An optimal solution maximizes the aggregate
utilities of all grid users. Second, this paper proposes a new optimization-based grid resource pricing algorithm for allocating
resources to grid users while maximizing the revenue of grid providers. Simulation results show that our proposed algorithm
is more efficient than compared allocation scheme.
Li Chunlin received the ME in computer science from Wuhan Transportation University in 2000, and PhD degree in Computer Software and
Theory from Huazhong University of Science and Technology in 2003. She now is an associate professor of Computer Science in
Wuhan University of Technology. Her research interests include computational grid, distributed computing and mobile agent.
She has published over 15 papers in international journals.
Li Layuan received the BE degree in Communication Engineering from Harbin Institute of Military Engineering, China in 1970 and the
ME degree in Communication and Electrical Systems from Huazhong University of Science and Technology, China in 1982. Since
1982, he has been with the Wuhan University of Technology, China, where he is currently a Professor and PhD tutor of Computer
Science, and Editor in Chief of the Journal of WUT. He is Director of International Society of High-Technol and Paper Reviewer
of IEEE INFOCOM, ICCC and ISRSDC. His research interests include high speed computer networks, protocol engineering and image
processing. Professor Li has published over 150 technical papers and is the author of six books. He also was awarded the National
Special Prize by the Chinese Government in 1993. 相似文献
13.
Genetic operators for combinatorial optimization in TSP and microarray gene ordering 总被引:1,自引:0,他引:1
This paper deals with some new operators of genetic algorithms and demonstrates their effectiveness to the traveling salesman
problem (TSP) and microarray gene ordering. The new operators developed are nearest fragment operator based on the concept
of nearest neighbor heuristic, and a modified version of order crossover operator. While these result in faster convergence
of Genetic Algorithm (GAs) in finding the optimal order of genes in microarray and cities in TSP, the nearest fragment operator
can augment the search space quickly and thus obtain much better results compared to other heuristics. Appropriate number
of fragments for the nearest fragment operator and appropriate substring length in terms of the number of cities/genes for
the modified order crossover operator are determined systematically. Gene order provided by the proposed method is seen to
be superior to other related methods based on GAs, neural networks and clustering in terms of biological scores computed using
categorization of the genes.
Shubhra Sankar Ray is a Visiting Research Fellow at the Center for Soft Computing Research: A National Facility, Indian Statistical Institute,
Kolkata, India. He received the M.Sc. in Electronic Science and M.Tech in Radiophysics & Electronics from University of Calcutta,
Kolkata, India, in 2000 and 2002, respectively. Till March 2006, he had been a Senior Research Fellow of the Council of Scientific
and Industrial Research (CSIR), New Delhi, India, working at Machine Intelligence Unit, Indian Statistical Institute, India.
His research interests include bioinformatics, evolutionary computation, neural networks, and data mining.
Sanghamitra Bandyopadhyay is an Associate Professor at Indian Statistical Institute, Calcutta, India. She did her Bachelors in Physics and Computer
Science in 1988 and 1992 respectively. Subsequently, she did her Masters in Computer Science from Indian Institute of Technology
(IIT), Kharagpur in 1994 and Ph.D in Computer Science from Indian Statistical Institute, Calcutta in 1998.
She has worked in Los Alamos National Laboratory, Los Alamos, USA, in 1997, as a graduate research assistant, in the University
of New South Wales, Sydney, Australia, in 1999, as a post doctoral fellow, in the Department of Computer Science and Engineering,
University of Texas at Arlington, USA, in 2001 as a faculty and researcher, and in the Department of Computer Science and
Engineering, University of Maryland Baltimore County, USA, in 2004 as a visiting research faculty.
Dr. Bandyopadhyay is the first recipient of Dr. Shanker Dayal Sharma Gold Medal and Institute Silver Medal for being adjudged
the best all round post graduate performer in IIT, Kharagpur in 1994. She has received the Indian National Science Academy
(INSA) and the Indian Science Congress Association (ISCA) Young Scientist Awards in 2000, as well as the Indian National Academy
of Engineering (INAE) Young Engineers' Award in 2002. She has published over ninety articles in international journals, conference
and workshop proceedings, edited books and journal special issues and served as the Program Co-Chair of the 1st International
Conference on Pattern Recognition and Machine Intelligence, 2005, Kolkata, India, and as the Tutorial Co-Chair, World Congress
on Lateral Computing, 2004, Bangalore, India. She is on the editorial board of the International Journal on Computational
Intelligence. Her research interests include Evolutionary and Soft Computation, Pattern Recognition, Data Mining, Bioinformatics,
Parallel & Distributed Systems and VLSI.
Sankar K. Pal (www.isical.ac.in/∼sankar) is the Director and Distinguished Scientist of the Indian Statistical Institute. He has founded
the Machine Intelligence Unit, and the Center for Soft Computing Research: A National Facility in the Institute in Calcutta.
He received a Ph.D. in Radio Physics and Electronics from the University of Calcutta in 1979, and another Ph.D. in Electrical
Engineering along with DIC from Imperial College, University of London in 1982.
He worked at the University of California, Berkeley and the University of Maryland, College Park in 1986-87; the NASA Johnson
Space Center, Houston, Texas in 1990-92 & 1994; and in US Naval Research Laboratory, Washington DC in 2004. Since 1997 he
has been serving as a Distinguished Visitor of IEEE Computer Society (USA) for the Asia-Pacific Region, and held seve ral
visiting positions in Hong Kong and Australian universities. Prof. Pal is a Fellow of the IEEE, USA, Third World Academy of
Sciences, Italy, International Association for Pattern recognition, USA, and all the four National Academies for Science/Engineering
in India. He is a co-author of thirteen books and about three hundred research publications in the areas of Pattern Recognition
and Machine Learning, Image Processing, Data Mining and Web Intelligence, Soft Computing, Neural Nets, Genetic Algorithms,
Fuzzy Sets, Rough Sets, and Bioinformatics.
He has received the 1990 S.S. Bhatnagar Prize (which is the most coveted award for a scientist in India), and many prestigious
awards in India and abroad including the 1999 G.D. Birla Award, 1998 Om Bhasin Award, 1993 Jawaharlal Nehru Fellowship, 2000
Khwarizmi International Award from the Islamic Republic of Iran, 2000–2001 FICCI Award, 1993 Vikram Sarabhai Research Award,
1993 NASA Tech Brief Award (USA), 1994 IEEE Trans. Neural Networks Outstanding Paper Award (USA), 1995 NASA Patent Application
Award (USA), 1997 IETE-R.L. Wadhwa Gold Medal, the 2001 INSA-S.H. Zaheer Medal, and 2005-06 P.C. Mahalanobis Birth Centenary
Award (Gold Medal) for Lifetime Achievement .
Prof. Pal is an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Trans. Neural Networks [1994–98,
2003–06], Pattern Recognition Letters, Neurocomputing (1995–2005), Applied Intelligence, Information Sciences, Fuzzy Sets
and Systems, Fundamenta Informaticae, Int. J. Computational Intelligence and Applications, and Proc. INSA-A; a Member, Executive
Advisory Editorial Board, IEEE Trans. Fuzzy Systems, Int. Journal on Image and Graphics, and Int. Journal of Approximate Reasoning;
and a Guest Editor of IEEE Computer. 相似文献
14.
Theory of relative defect proneness 总被引:1,自引:1,他引:0
A. Güneş Koru Khaled El Emam Dongsong Zhang Hongfang Liu Divya Mathew 《Empirical Software Engineering》2008,13(5):473-498
In this study, we investigated the functional form of the size-defect relationship for software modules through replicated
studies conducted on ten open-source products. We consistently observed a power-law relationship where defect proneness increases
at a slower rate compared to size. Therefore, smaller modules are proportionally more defect prone. We externally validated
the application of our results for two commercial systems. Given limited and fixed resources for code inspections, there would
be an impressive improvement in the cost-effectiveness, as much as 341% in one of the systems, if a smallest-first strategy
were preferred over a largest-first one. The consistent results obtained in this study led us to state a theory of relative
defect proneness (RDP): In large-scale software systems, smaller modules will be proportionally more defect-prone compared
to larger ones. We suggest that practitioners consider our results and give higher priority to smaller modules in their focused
quality assurance efforts.
A. Güneş Koru received a B.S. degree in Computer Engineering from Ege University, İzmir, Turkey in 1996, an M.S. degree in Computer Engineering from Dokuz Eylül University, İzmir, Turkey in 1998, an M.S. degree in Software Engineering from Southern Methodist University (SMU), Dallas, TX in 2002, and a Ph.D. degree in Computer Science from SMU in 2004. He is an assistant professor in the Department of Information Systems at University of Maryland, Baltimore County (UMBC). His research interests include software quality, measurement, maintenance, and evolution, open source software, bioinformatics, and healthcare informatics. Khaled El Emam is an Associate Professor at the University of Ottawa, Faculty of Medicine and the School of Information Technology and Engineering. He is a Canada Research Chair in Electronic Health Information at the University of Ottawa. Previously Khaled was a Senior Research Officer at the National Research Council of Canada, and prior to that he was head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany. In 2003 and 2004, he was ranked as the top systems and software engineering scholar worldwide by the Journal of Systems and Software based on his research on measurement and quality evaluation and improvement, and ranked second in 2002 and 2005. He holds a Ph.D. from the Department of Electrical and Electronics, King’s College, at the University of London (UK). His labs web site is: . Dongsong Zhang is an Associate Professor in the Department of Information Systems at University of Maryland, Baltimore County. He received his Ph.D. in Management Information Systems from the University of Arizona. His current research interests include context-aware mobile computing, computer-mediated collaboration and communication, knowledge management, and open source software. Dr. Zhang’s work has been published or will appear in journals such as Communications of the ACM (CACM), Journal of Management Information Systems (JMIS), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Multimedia, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Transactions on Professional Communication, among others. He has received research grants and awards from NIH, Google Inc., and Chinese Academy of Sciences. He also serves as senior editor or editorial board member of a number of journals. Hongfang Liu is currently an Assistant Professor in Department of Biostatistics, Bioinformatics, and Biomathematics (DBBB) of Georgetown University. She has been working in the field of Biomedical Informatics for more than 10 years. Her expertise in clinical informatics includes clinical information system, controlled medical vocabulary, and medical language processing. Her expertise in bioinformatics includes microarray data analysis, biomedical entity nomenclature, molecular biology database curation, ontology, and biological text mining. She received a B.S. degree in Applied Mathematics and Statistics from University of Science and Technology of China in 1994, a M.S. degree in Computer Science from Fordham University in 1998, a PhD degree in computer science at the Graduate School of City University of New York in 2002. Divya Mathew received the BTech degree in computer science and engineering from Cochin University of Science and Technology in 2005 and the MS degree in information systems from the University of Maryland, Baltimore County in 2008. Her research interests include software engineering and privacy preserving data mining techniques. 相似文献
Divya MathewEmail: |
A. Güneş Koru received a B.S. degree in Computer Engineering from Ege University, İzmir, Turkey in 1996, an M.S. degree in Computer Engineering from Dokuz Eylül University, İzmir, Turkey in 1998, an M.S. degree in Software Engineering from Southern Methodist University (SMU), Dallas, TX in 2002, and a Ph.D. degree in Computer Science from SMU in 2004. He is an assistant professor in the Department of Information Systems at University of Maryland, Baltimore County (UMBC). His research interests include software quality, measurement, maintenance, and evolution, open source software, bioinformatics, and healthcare informatics. Khaled El Emam is an Associate Professor at the University of Ottawa, Faculty of Medicine and the School of Information Technology and Engineering. He is a Canada Research Chair in Electronic Health Information at the University of Ottawa. Previously Khaled was a Senior Research Officer at the National Research Council of Canada, and prior to that he was head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany. In 2003 and 2004, he was ranked as the top systems and software engineering scholar worldwide by the Journal of Systems and Software based on his research on measurement and quality evaluation and improvement, and ranked second in 2002 and 2005. He holds a Ph.D. from the Department of Electrical and Electronics, King’s College, at the University of London (UK). His labs web site is: . Dongsong Zhang is an Associate Professor in the Department of Information Systems at University of Maryland, Baltimore County. He received his Ph.D. in Management Information Systems from the University of Arizona. His current research interests include context-aware mobile computing, computer-mediated collaboration and communication, knowledge management, and open source software. Dr. Zhang’s work has been published or will appear in journals such as Communications of the ACM (CACM), Journal of Management Information Systems (JMIS), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Multimedia, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Transactions on Professional Communication, among others. He has received research grants and awards from NIH, Google Inc., and Chinese Academy of Sciences. He also serves as senior editor or editorial board member of a number of journals. Hongfang Liu is currently an Assistant Professor in Department of Biostatistics, Bioinformatics, and Biomathematics (DBBB) of Georgetown University. She has been working in the field of Biomedical Informatics for more than 10 years. Her expertise in clinical informatics includes clinical information system, controlled medical vocabulary, and medical language processing. Her expertise in bioinformatics includes microarray data analysis, biomedical entity nomenclature, molecular biology database curation, ontology, and biological text mining. She received a B.S. degree in Applied Mathematics and Statistics from University of Science and Technology of China in 1994, a M.S. degree in Computer Science from Fordham University in 1998, a PhD degree in computer science at the Graduate School of City University of New York in 2002. Divya Mathew received the BTech degree in computer science and engineering from Cochin University of Science and Technology in 2005 and the MS degree in information systems from the University of Maryland, Baltimore County in 2008. Her research interests include software engineering and privacy preserving data mining techniques. 相似文献
15.
Internet video streaming is a widely popular application however, in many cases, congestion control facilities are not well
integrated into such applications. In order to be fair to other users that do not stream video, rate adaptation should be
performed to respond to congestion. On the other hand, the effect of rate adaptation on the viewer should be minimized and
this extra mechanism should not overload the client and the server. In this paper, we develop a heuristic approach for unicast
congestion control. The primary feature of our approach is the two level adaptation algorithm that utilizes packet loss rate
as well as receiver buffer data to maintain satisfactory buffer levels at the receiver. This is particularly important if
receiver has limited buffer such as in mobile devices. When there is no congestion, to maintain best buffer levels, fine grain
adjustments are carried out at the packet level. Depending on the level of congestion and receiver buffer level, rate shaping
that involves frame discard and finally rate adaptation by switching to a different pre-encoded video stream are carried out.
Additive increase multiplicative decrease policy is maintained to respond to congestion in a TCP- friendly manner. The algorithm
is implemented and performance results show that it has adaptation ability that is suitable for both local area and wide area
networks.
E. Turhan Tunali received B.Sc. Degree in Electrical Engineering from Middle East Technical University and M.Sc. Degree in Applied Statistics
from Ege University, both in Turkey. He then received D.Sc. Degree in Systems Science and Mathematics from Washington University
in St. Louis, U.S.A. in 1985. After his doctorate study, he joined Computer Engineering Department of Ege University as an
assistant professor where he became an associate professor in 1988. During the period of 1992–1994, he worked in Department
of Computer Technology of Nanyang Technological University of Singapore as a Visiting Senior Fellow. He then joined International
Computer Institute of Ege University as a Professor where he is currently the director. In the period of 2000–2001 he worked
in Department of Computer Science of Loyola University of Chicago as a Visiting Professor. His current research interests
include adaptive video streaming and Internet performance measurements. Dr. Tunali is married with an eighteen year old son.
Aylin Kantarci received B.Sc., M.Sc. and Ph.D. degrees all from Computer Engineering Department of Ege University, Izmir, Turkey, in 1992,
1994 and 2000, respectively. She then joined the same department as an assistant professor. Her current research interests
include adaptive video streaming, video coding, operating systems, multimedia systems and distributed systems.
Nukhet Ozbek received B.Sc. degree in Electrical and Electronics Engineering from School of Engineering and M.Sc. degree in Computer Science
from International Computer Institute both in Ege University, Izmir, Turkey. From 1998 to 2003 she worked in the DVB team
of Digital R&D at Vestel Corporation, Izmir-Turkey that produces telecommunication and consumer electronics devices. She is
currently a Ph.D. student and a research assistant at International Computer Institute of Ege University. Her research areas
include video coding and streaming, multimedia systems and set top box architectures. 相似文献
16.
Ensuring causal consistency in a Distributed Shared Memory (DSM) means all operations executed at each process will be compliant
to a causality order relation. This paper first introduces an optimality criterion for a protocol P, based on a complete replication of variables at each process and propagation of write updates, that enforces causal consistency.
This criterion measures the capability of a protocol to update the local copy as soon as possible while respecting causal
consistency. Then we present an optimal protocol built on top of a reliable broadcast communication primitive and we show
how previous protocols based on complete replication presented in the literature are not optimal. Interestingly, we prove
that the optimal protocol embeds a system of vector clocks which captures the read/write semantics of a causal memory. From
an operational point of view, an optimal protocol strongly reduces its message buffer overhead. Simulation studies show that
the optimal protocol roughly buffers a number of messages of one order of magnitude lower than non-optimal ones based on the
same communication primitive.
R. Baldoni Roberto Baldoni is a Professor of Distributed Systems at the University of Rome “La Sapienza”. He published more than one
hundred papers (from theory to practice) in the fields of distributed and mobile computing, middleware platforms and information
systems. He is the founder of MIDdleware LABoratory <://www.dis.uniroma1.it/$∼midlab> textgreater (MIDLAB) whose members
participate to national and european research projects. He regularly serves as an expert for the EU commission in the evaluation
of EU projects. Roberto Baldoni chaired the program committee of the “distributed algorithms” track of the 19th IEEE International
Conference on Distributed Computing Systems (ICDCS-99) and /he was PC Co-chair of the ACM International Workshop on Principles
of Mobile Computing/ (POMC). He has been also involved in the organizing and program committee of many premiership international
conferences and workshops.
A. Milani Alessia Milani is currently involved in a joint research doctoral thesis between the Department of Computer and Systems Science
of the University of Rome “La Sapienza” and the University of Rennes I, IRISA.She earned a Laurea degree in Computer Engineering
at University of Rome “La Sapienza” on May 2003. Her research activity involves the area of distributed systems. Her current
research interests include communication paradigms, in particular distributed shared memories, replication and consistency
criterions.
S. Tucci Piergiovanni Sara Tucci Piergiovanni is currently a Ph.D. Student at the Department of Computer and Systems Science of the University
of Rome “La Sapienza”.She earned a Laurea degree in Computer Engineering at University of Rome “La Sapienza” on March 2002
with marks 108/110. Her laurea thesis has been awarded the italian national “Federcommin-AICA” prize 2002 for best laurea
thesis in Information Technology. Her research activity involves the area of distributed systems. Early works involved the
issue of fault-tolerance in asynchronous systems and software replication. Currently, her main focus is on communication paradigms
that provide an “anonymous” communication as publish/subscribe and distributed shared memories. The core contributions are
several papers published in international conferences and journals. 相似文献
17.
Saharon Rosset Claudia Perlich Bianca Zadrozny 《Knowledge and Information Systems》2007,12(3):331-353
We suggest the use of ranking-based evaluation measures for regression models, as a complement to the commonly used residual-based
evaluation. We argue that in some cases, such as the case study we present, ranking can be the main underlying goal in building
a regression model, and ranking performance is the correct evaluation metric. However, even when ranking is not the contextually
correct performance metric, the measures we explore still have significant advantages: They are robust against extreme outliers
in the evaluation set; and they are interpretable. The two measures we consider correspond closely to non-parametric correlation
coefficients commonly used in data analysis (Spearman's ρ and Kendall's τ); and they both have interesting graphical representations,
which, similarly to ROC curves, offer useful various model performance views, in addition to a one-number summary in the area
under the curve. An interesting extension which we explore is to evaluate models on their performance in “partially” ranking
the data, which we argue can better represent the utility of the model in many cases. We illustrate our methods on a case
study of evaluating IT Wallet size estimation models for IBM's customers.
Saharon Rosset is Research Staff Member in the Data Analytics Research Group at IBM's T. J. Watson Research Center. He received his B.S.
in Mathematics and M.Sc., in Statistics from Tel Aviv University in Israel, and his Ph.D. in Statistics from Stanford University
in 2003. In his research, he aspires to develop practically useful predictive modeling methodologies and tools, and apply
them to solve problems in business and scientific domains. Currently, his major projects include work on customer wallet estimation
and analysis of genetic data.
Claudia Perlich has received a M.Sc. in Computer Science from Colorado University at Boulder, a Diploma in Computer Science from Technische
Universitaet in Darmstadt, and her Ph.D. in Information Systems from Stern School of Business, New York University. Her Ph.D.
thesis concentrated on probability estimation in multi-relational domains that capture information of multiple entity types
and relationships between them. Her dissertation was recognized as an additional winner of the International SAP Doctoral
Support Award Competition. Claudia joined the Data Analytics Research group at IBM's T.J. Watson Research Center as a Research
Staff Member in October 2004. Her research interests are in statistical machine learning for complex real-world domains and
business applications.
Bianca Zadrozny is currently an associate professor at the Computer Science Department of Federal Fluminense University in Brazil. Her research
interests are in the areas of applied machine learning and data mining. She received her B.Sc. in Computer Engineering from
the Pontifical Catholic University in Rio de Janeiro, Brazil, and her M.Sc. and Ph.D. in Computer Science from the University
of California at San Diego. She has also worked as a research staff member in the data analytics research group at IBM T.J.
Watson Research Center. 相似文献
18.
In software testing, developing effective debugging strategies is important to guarantee the reliability of software under
testing. A heuristic technique is to cause failure and therefore expose faults. Based on this approach mutation testing has
been found very useful technique in detecting faults. However, it suffers from two problems with successfully testing programs:
(1) requires extensive computing resources and (2) puts heavy demand on human resources. Later, empirical observations suggest
that critical slicing based on Statement Deletion (Sdl) mutation operator has been found the most effective technique in reducing
effort and the required computing resources in locating the program faults. The second problem of mutation testing may be
solved by automating the program testing with the help of software tools. Our study focuses on determining the effectiveness
of the critical slicing technique with the help of the Mothra Mutation Testing System in detecting program faults. This paper
presents the results showing the performance of Mothra Mutation Testing System through conducting critical slicing testing
on a selected suite of programs.
Zuhoor Abdullah Al-Khanjari is an assistant professor in the Computer Science Department at Sultan Qaboos University, Sultanate of Oman. She received
her BSc in mathematics and computing from Sultan Qaboos University, MSc and PhD in Computer Science (Software Engineering)
from the University of Liverpool, UK. Her research interests include software testing, database management, e-learning, human-computer
interaction, programming languages, intelligent search engines, and web data mining and development. ~Currently, she is the
coordinator of the software engineering research group in the Department of Computer Science, College of Science, Sultan Qaboos
University. She is also coordinating a program to develop e-learning based undergraduate teaching in the Department of Computer
Science. Currently she is holding the position of assistant dean for postgraduate studies and research in the College of Science,
Sultan Qaboos University, Sultanate of Oman.
Martin Woodward is a Senior Fellow in the Computer Science Department at the University of Liverpool in the UK. After obtaining BSc and Ph.D.
degrees in mathematics from the University of Nottingham, he was employed by the University of Oxford as a Research Assistant
on secondment to the UK Atomic Energy Authority at the Culham Laboratory. He has been at the University of Liverpool for many
years and initially worked on the so-called ‘Testbed’ project, helping to develop automated tools for software testing which
are now marketed successfully by a commercial organisation. His research interests include software testing techniques, the
relationship between formal methods and testing, and software visualisation. He has served as Editor of the journal ‘Software
Testing, Verification and Reliability’ for the past thirteen years.
Haider Ramadhan is an associate professor in the Computer Science Department at Sultan Qaboos University. He received his BS and MS in Computer
Science from University of North Carolina, and the PhD in Computer Science and AI from Sussex University. His research interests
include visualization of software, systems, and process, system engineering, human-computer interaction, intelligent search
engines, and Web data mining and development. Currently, he is the chairman of the Computer Science Department, College of
Science, Sultan Qaboos University, Sultanate of Oman.
Swamy Kutti
(N. S. Kutti) is an associate professor in the Computer Science Department at Sultan Qaboos University. He received his B.E. in Electronics
Engineering from the University of Madras, M.E. in Communication Engineering from Indian Institute of Science (Bangalore),
and the MSc in Computer Science from Monash University (Australia) and PhD in Computer Science from Deakin University (Australia).
His research interests include Real-Time Programming, Programming Languages, Program Testing and Verification, eLearning,
and Distributed Operating Systems. 相似文献
19.
This paper addresses the problem of resource allocation for distributed real-time periodic tasks, operating in environments
that undergo unpredictable changes and that defy the specification of meaningful worst-case execution times. These tasks are
supplied by input data originating from various environmental workload sources. Rather than using worst-case execution times
(WCETs) to describe the CPU usage of the tasks, we assume here that execution profiles are given to describe the running time
of the tasks in terms of the size of the input data of each workload source. The objective of resource allocation is to produce
an initial allocation that is robust against fluctuations in the environmental parameters. We try to maximize the input size
(workload) that can be handled by the system, and hence to delay possible (costly) reallocations as long as possible. We present
an approximation algorithm based on first-fit and binary search that we call FFBS. As we show here, the first-fit algorithm
produces solutions that are often close to optimal. In particular, we show analytically that FFBS is guaranteed to produce
a solution that is at least 41% of optimal, asymptotically, under certain reasonable restrictions on the running times of
tasks in the system. Moreover, we show that if at most 12% of the system utilization is consumed by input independent tasks
(e.g., constant time tasks), then FFBS is guaranteed to produce a solution that is at least 33% of optimal, asymptotically.
Moreover, we present simulations to compare FFBS approximation algorithm with a set of standard (local search) heuristics
such as hill-climbing, simulated annealing, and random search. The results suggest that FFBS, in combination with other local
improvement strategies, may be a reasonable approach for resource allocation in dynamic real-time systems.
David Juedes is a tenured associate professor and assistant chair for computer science in the School of Electrical Engineering and Computer
Science at Ohio University. Dr. Juedes received his Ph.D. in Computer Science from Iowa State University in 1994, and his
main research interests are algorithm design and analysis, the theory of computation, algorithms for real-time systems, and
bioinformatics. Dr. Juedes has published numerous conference and journal papers and has acted as a referee for IEEE Transactions
on Computers, Algorithmica, SIAM Journal on Computing, Theoretical Computer Science, Information and Computation, Information
Processing Letters, and other conferences and journals.
Dazhang Gu is a software architect and researcher at Pegasus Technologies (NeuCo), Inc. He received his Ph.D. in Electrical Engineering
and Computer Science from Ohio University in 2005. His main research interests are real-time systems, distributed systems,
and resource optimization. He has published conference and journal papers on these subjects and has refereed for the Journal
of Real-Time Systems, IEEE Transactions on Computers, and IEEE Transactions on Parallel and Distributed Systems among others.
He also served as a session chair and publications chair for several conferences.
Frank Drews is an Assistant Professor of Electical Engineering and Computer Science at Ohio Unversity. Dr. Drews received his Ph.D. in
Computer Science from the Clausthal Unversity of Technolgy in Germany in 2002. His main research interests are resource management
for operating systems and real-time systems, and bioinformatics. Dr. Drews has numerous publications in conferences and journals
and has served as a reviewer for IEEE Transactions on Computers, the Journal of Systems and Software, and other conferences
and Journals. He was Publication Chair for the OCCBIO’06 conference, Guest Editor of a Special Issue of the Journal of Systems
and Software on “Dynamic Resource Management for Distributed Real-Time Systems”, organizer of special tracks at the IEEE IPDPS
WPDRTS workshops in 2005 and 2006.
Klaus Ecker received his Ph.D. in Theoretical Physics from the University of Graz, Austria, and his Dr. habil. in Computer Science from
the University of Bonn. Since 1978 he is professor in the Department of Computer Science at the Clausthal University of Technology,
Germany, and since 2005 he is visiting professor at the Ohio University. His research interests are parallel processing and
theory of scheduling, especially in real time systems, and bioinformatics. Prof. Ecker published widely in the above mentioned
areas in well reputed journals and proceedings of international conferences as well. He is also the author of two monographs
on scheduling theory. Since 1981 he is organizing annually international workshops on parallel processing. He is associate
editor of Real Time Systems, and member of the German Gesellschaft fuer Informatik (GI) and of the Association for Computing
Machinery (ACM).
Lonnie R. Welch received a Ph.D. in Computer and Information Science from the Ohio State University. Currently, he is the Stuckey Professor
of Electrical Engineering and Computer Science at Ohio University. Dr. Welch performs research in the areas of real-time systems,
distributed computing and bioinformatics. His research has been sponsored by the Defense Advanced Research Projects Agency,
the Navy, NASA, the National Science Foundation and the Army. Dr. Welch has twenty years of research experience in the area
of high performance computing. In his graduate work at Ohio State University, he developed a high performance 3-D graphics
rendering algorithm, and he invented a parallel virtual machine for object-oriented software. For the past 15 years his research
has focused on middleware and optimization algorithms for high performance computing. His research has produced three successive
generations of adaptive resource management (RM) middleware for high performance real-time systems. The project has resulted
in two patents and more than 150 publications. Professor Welch also collaborates on diabetes research with faculty at Edison
Biotechnology Institute and on genomics research with faculty in the Department of Environmental and Plant Biology at Ohio
University. Dr. Welch is a member of the editorial boards of IEEE Transactions on Computers, The Journal of Scalable Computing:
Practice and Experience, and The International Journal of Computers and Applications. He is also the founder of the International
Workshop on Parallel and Distributed Real-time Systems and of the Ohio Collaborative Conference on Bioinformatics.
Silke Schomann graduated in 2003 with a M.Sc. in Computer Science from Clausthal University Of Technology, where she has been working as
a scientific assistant since then. She is currently working on her Ph.D. thesis in computer science at the same university. 相似文献
20.
The resources allocated for software quality assurance and improvement have not increased with the ever-increasing need for
better software quality. A targeted software quality inspection can detect faulty modules and reduce the number of faults
occurring during operations. We present a software fault prediction modeling approach with case-based reasoning (CBR), a part
of the computational intelligence field focusing on automated reasoning processes. A CBR system functions as a software fault
prediction model by quantifying, for a module under development, the expected number of faults based on similar modules that
were previously developed. Such a system is composed of a similarity function, the number of nearest neighbor cases used for
fault prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm may affect
the performance accuracy of a CBR-based software fault prediction system. This paper presents an empirical study investigating
the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy
of our CBR system. The influence of varying the number of nearest neighbor cases on the performance accuracy is also explored.
Moreover, the benefits of using metric-selection procedures for our CBR system is also evaluated. Case studies of a large
legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance
similarity function and the inverse distance weighted solution algorithm yielded the best fault prediction. In addition, the
CBR models have better performance than models based on multiple linear regression.
Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the
Empirical Software Engineering Laboratory. His research interests are in software engineering, software metrics, software
reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, and statistical
modeling. He has published more than 200 refereed papers in these areas. He has been a principal investigator and project
leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the Association
for Computing Machinery, the IEEE Computer Society, and IEEE Reliability Society. He served as the general chair of the 1999
International Symposium on Software Reliability Engineering (ISSRE’99), and the general chair of the 2001 International Conference
on Engineering of Computer Based Systems. Also, he has served on technical program committees of various international conferences,
symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards
of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems.
Naeem Seliya received the M.S. degree in Computer Science from Florida Atlantic University, Boca Raton, FL, USA, in 2001. He is currently
a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests
include software engineering, computational intelligence, data mining, software measurement, software reliability and quality
engineering, software architecture, computer data security, and network intrusion detection. He is a student member of the
IEEE Computer Society and the Association for Computing Machinery. 相似文献