共查询到10条相似文献,搜索用时 140 毫秒
1.
Privacy-preserving SVM classification 总被引:2,自引:2,他引:0
Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or
in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects.
What is required is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results,
while providing guarantees on the nondisclosure of data. Support vector machine classification is one of the most widely used
classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide
practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification,
PP-SVM for short. Our solution constructs the global SVM classification model from data distributed at multiple parties, without
disclosing the data of each party to others. Solutions are sketched out for data that is vertically, horizontally, or even
arbitrarily partitioned. We quantify the security and efficiency of the proposed method, and highlight future challenges.
Jaideep Vaidya received the Bachelor’s degree in Computer Engineering from the University of Mumbai. He received the Master’s and the Ph.D.
degrees in Computer Science from Purdue University. He is an Assistant Professor in the Management Science and Information
Systems Department at Rutgers University. His research interests include data mining and analysis, information security, and
privacy. He has received best paper awards for papers in ICDE and SIDKDD. He is a Member of the IEEE Computer Society and
the ACM.
Hwanjo Yu received the Ph.D. degree in Computer Science in 2004 from the University of Illinois at Urbana-Champaign. He is an Assistant
Professor in the Department of Computer Science at the University of Iowa. His research interests include data mining, machine
learning, database, and information systems. He is an Associate Editor of Neurocomputing and served on the NSF Panel in 2006.
He has served on the program committees of 2005 ACM SAC on Data Mining track, 2005 and 2006 IEEE ICDM, 2006 ACM CIKM, and
2006 SIAM Data Mining.
Xiaoqian Jiang received the B.S. degree in Computer Science from Shanghai Maritime University, Shanghai, 2003. He received the M.C.S. degree
in Computer Science from the University of Iowa, Iowa City, 2005. Currently, he is pursuing a Ph.D. degree from the School
of Computer Science, Carnegie Mellon University. His research interests are computer vision, machine learning, data mining,
and privacy protection technologies. 相似文献
2.
Recently, mining from data streams has become an important and challenging task for many real-world applications such as credit
card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier
from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a new dynamic
classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm
dynamically selects a single “best” classifier to classify each test instance at run time. Our scheme uses statistical information
from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure
that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute
values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the
highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative
studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams
with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or
have low confidence.
A preliminary version of this paper was published in the Proceedings of the 4th IEEE International Conference on Data Mining,
pp 305–312, Brighton, UK
Xingquan Zhu received his Ph.D. degree in Computer Science from Fudan University, Shanghai, China, in 2001. He spent four months with
Microsoft Research Asia, Beijing, China, where he was working on content-based image retrieval with relevance feedback. From
2001 to 2002, he was a Postdoctoral Associate in the Department of Computer Science, Purdue University, West Lafayette, IN.
He is currently a Research Assistant Professor in the Department of Computer Science, University of Vermont, Burlington, VT.
His research interests include Data mining, machine learning, data quality, multimedia computing, and information retrieval.
Since 2000, Dr. Zhu has published extensively, including over 40 refereed papers in various journals and conference proceedings.
Xindong Wu is a Professor and the Chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial
Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems,
and Web information exploration. He has published extensively in these areas in various journals and conferences, including
IEEE TKDE, TPAMI, ACM TOIS, IJCAI, ICML, KDD, ICDM, and WWW, as well as 11 books and conference proceedings. Dr. Wu is the
Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the founder and current Steering Committee Chair of the IEEE International Conference on
Data Mining (ICDM), an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP).
He is the 2004 ACM SIGKDD Service Award winner.
Ying Yang received her Ph.D. in Computer Science from Monash University, Australia in 2003. Following academic appointments at the
University of Vermont, USA, she currently holds a Research Fellow at Monash University, Australia. Dr. Yang is recognized
for contributions in the fields of machine learning and data mining. She has published many scientific papers and book chapters
on adaptive learning, proactive mining, noise cleansing and discretization. Contact her at yyang@mail.csse.monash.edu.au. 相似文献
3.
Mining frequent patterns with a frequent pattern tree (FP-tree in short) avoids costly candidate generation and repeatedly
occurrence frequency checking against the support threshold. It therefore achieves much better performance and efficiency
than Apriori-like algorithms. However, the database still needs to be scanned twice to get the FP-tree. This can be very time-consuming
when new data is added to an existing database because two scans may be needed for not only the new data but also the existing
data. In this research we propose a new data structure, the pattern tree (P-tree in short), and a new technique, which can
get the P-tree through only one scan of the database and can obtain the corresponding FP-tree with a specified support threshold.
Updating a P-tree with new data needs one scan of the new data only, and the existing data does not need to be re-scanned.
Our experiments show that the P-tree method outperforms the FP-tree method by a factor up to an order of magnitude in large
datasets.
A preliminary version of this paper has been published in theProceedings of the 2002 IEEE International Conference on Data Mining (ICDM ’02), 629–632.
Hao Huang: He is pursuing his Ph.D. degree in the Department of Computer Science at the University of Virginia. His research interests
are Gird Computing, Data Mining and their applications in Bioinformatics. He received his M.S. in Computer Science from Colorado
School of Mines in 2001.
Xindong Wu, Ph.D.: He is Professor and Chair of the Department of Computer Science at the University of Vermont, USA. He holds a Ph.D. in Artificial
Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems,
and Web information exploration. He has published extensively in these areas in various journals and conferences, including
IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM, and WWW. Dr. Wu is the Executive Editor (January 1, 1999-December
31, 2004) and an Honorary Editor-in-Chief (starting January 1, 2005) of Knowledge and Information Systems (a peer-reviewed
archival journal published by Springer), the founder and current Steering Committee Chair of the IEEE International Conference
on Data Mining (ICDM), a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP),
and the Chair of the IEEE Computer Society Technical Committee on Computational Intelligence (TCCI). He served as an Associate
Editor for the IEEE Transactions on Knowledge and Data Engineering (TKDE) between January 1, 2000 and December 31, 2003, and
is the Editor-in-Chief of TKDE since January 1, 2005. He is the winner of the 2004 ACM SIGKDD Service Award.
Richard Relue, Ph.D.: He received his Ph.D. in Computer Science from the Colorado School of Mines in 2003. His research interests include association
rules in data mining, neural networks for automated classification, and artificial intelligence for robot navigation. He has
been an Information Technology consultant since 1992, working with Ball Aerospace and Technology, Rational Software, Natural
Fuels Corporation, and Western Interstate Commission for Higher Education (WICHE). 相似文献
4.
Efficient string matching with wildcards and length constraints 总被引:1,自引:2,他引:1
Gong Chen Xindong Wu Xingquan Zhu Abdullah N. Arslan Yu He 《Knowledge and Information Systems》2006,10(4):399-419
This paper defines a challenging problem of pattern matching between a pattern P and a text T, with wildcards and length constraints, and designs an efficient algorithm to return each pattern occurrence in an online manner. In this pattern matching problem, the user can specify the constraints on the number of wildcards between each two consecutive letters of P and the constraints on the length of each matching substring in T. We design a complete algorithm, SAIL that returns each matching substring of P in T as soon as it appears in T in an O(n+klmg) time with an O(lm) space overhead, where n is the length of T, k is the frequency of P's last letter occurring in T, l is the user-specified maximum length for each matching substring, m is the length of P, and g is the maximum difference between the user-specified maximum and minimum numbers of wildcards allowed between two consecutive letters in P.SAIL stands for string matching with wildcards and length constraints.
Gong Chen received the B.Eng. degree from the Beijing University of Technology, China, and the M.Sc. degree from the University of Vermont, USA, both in computer science. He is currently a graduate student in the Department of Statistics at the University of California, Los Angeles, USA. His research interests include data mining, statistical learning, machine learning, algorithm analysis and design, and database management.
Xindong Wu is a professor and the chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM and WWW, as well as 12 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM),an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He is the 2004 ACM SIGKDD Service Award winner.
Xingquan Zhu received his Ph.D degree in Computer Science from Fudan University, Shanghai, China, in 2001. He spent 4 months with Microsoft Research Asia, Beijing, China, where he was working on content-based image retrieval with relevance feedback. From 2001 to 2002, he was a postdoctoral associate in the Department of Computer Science at Purdue University, West Lafayette, IN. He is currently a research assistant professor in the Department of Computer Science, the University of Vermont, Burlington, VT. His research interests include data mining, machine learning, data quality, multimedia computing, and information retrieval. Since 2000, Dr. Zhu has published extensively, including over 50 refereed papers in various journals and conference proceedings.
Abdullah N. Arslan got his Ph.D. degree in Computer Science in 2002 from the University of California at Santa Barbara. Upon his graduation he joined the Department of Computer Science at the University of Vermont as an assistant professor. He has been with the computer science faculty there since then. Dr. Arslan's main research interests are on algorithms on strings, computational biology and bioinformatics. Dr. Arslan earned his Master's degree in Computer Science in 1996 from the University of North Texas, Denton, Texas and his Bachelor's degree in Computer Engineering in 1990 from the Middle East Technical University, Ankara, Turkey. He worked as a programmer for the Central Bank of Turkey between 1991 and 1994.
Yu He received her B.E. degree in Information Engineering from Zhejiang University, China, in 2001. She is currently a graduate student in the Department of Computer Science at the University of Vermont. Her research interests include data mining, bioinformatics and pattern recognition. 相似文献
5.
6.
Roger Zimmermann Cyrus Shahabi Kun Fu Mehrdad Jahangiri 《Multimedia Tools and Applications》2006,28(1):23-49
Variable bit rate (VBR) compression for media streams allocates more bits to complex scenes and fewer bits to simple scenes.
This results in a higher and more uniform visual and aural quality. The disadvantage of the VBR technique is that it results
in bursty network traffic and uneven resource utilization when streaming media. In this study we propose an online media transmission
smoothing technique that requires no a priori knowledge of the actual bit rate. It utilizes multi-level buffer thresholds
at the client side that trigger feedback information sent to the server. This technique can be applied to both live captured
streams and stored streams without requiring any server side pre-processing. We have implemented this scheme in our continuous
media server and verified its operation across real world LAN and WAN connections. The results show smoother transmission
schedules than any other previously proposed online technique.
This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC), and IIS-0082826, DARPA and USAF under agreement
nr. F30602-99-1-0524, and unrestricted cash/equipment gifts from NCR, IBM, Intel and SUN.
Roger Zimmermann is currently a Research Assistant Professor with the Computer Science Department and a Research Area Director with the Integrated
Media Systems Center (IMSC) at the University of Southern California. His research activities focus on streaming media architectures,
peer-to-peer systems, immersive environments, and multimodal databases. He has made significant contributions in the areas
of interactive and high quality video streaming, collaborative large-scale group communications, and mobile location-based
services. Dr. Zimmermann has co-authored a book, a patent and more than seventy conference publications, journal articles
and book chapters in the areas of multimedia and databases. He was the co-chair of the ACM NRBC 2004 workshop, the Open Source
Software Competition of the ACM Multimedia 2004 conference, the short paper program systems track of ACM Multimedia 2005 and
will be the proceedings chair of ACM Multimedia 2006. He is on the editorial board of SIGMOD DiSC, the ACM Computers in Entertainment
magazine and the International Journal of Multimedia Tools and Applications. He has served on many conference program committees
such as ACM Multimedia, SPIE MMCN and IEEE ICME.
Cyrus Shahabi is currently an Associate Professor and the Director of the Information Laboratory (InfoLAB) at the Computer Science Department
and also a Research Area Director at the NSF's Integrated Media Systems Center (IMSC) at the University of Southern California.
He received his M.S. and Ph.D. degrees in Computer Science from the University of Southern California in May 1993 and August
1996, respectively. His B.S. degree is in Computer Engineering from Sharif University of Technology, Iran. He has two books
and more than hundred articles, book chapters, and conference papers in the areas of databases and multimedia. Dr. Shahabi's
current research interests include Peer-to-Peer Systems, Streaming Architectures, Geospatial Data Integration and Multidimensional
Data Analysis. He is currently an associate editor of the IEEE Transactions on Parallel and Distributed Systems (TPDS) and
on the editorial board of ACM Computers in Entertainment magazine. He is also the program committee chair of ICDE NetDB 2005
and ACM GIS 2005. He serves on many conference program committees such as IEEE ICDE 2006, ACM CIKM 2005, SSTD 2005 and ACM
SIGMOD 2004. Dr. Shahabi is the recipient of the 2002 National Science Foundation CAREER Award and 2003 Presidential Early
Career Awards for Scientists and Engineers (PECASE). In 2001, he also received an award from the Okawa Foundations.
Kun Fu is currently a Ph.D candidate in computer science from the University of Southern California. He did research at the Data
Communication Technology Research Institute and National Data Communication Engineering Center in China prior to coming to
the United States and is currently working on large scale data stream recording architectures at the NSF's Integrated Media
System Center (IMSC) and Data Management Research Laboratory (DMRL) at the Computer Science Department at USC. He received
an MS in engineering science from the University of Toledo. He is a member of the IEEE. His research interests are in the
area of scalable streaming architectures, distributed real-time systems, and multimedia computing and networking.
Mehrdad Jahangiri was born in Tehran, Iran. He received the B.S. degree in Civil Engineering from University of Tehran at Tehran, in 1999.
He is currently working towards the Ph.D. degree in Computer Science at the University of Southern California. He is currently
a research assistant working on multidimensional data analysis at Integrated Media Systems Center (IMSC)—Information Laboratory
(InfoLAB) at the Computer Science Department of the University of Southern California. 相似文献
7.
Wankang Zhao William Kreahling David Whalley Christopher Healy Frank Mueller 《Real-Time Systems》2006,34(2):129-152
It is advantageous to perform compiler optimizations that attempt to lower the worst-case execution time (WCET) of an embedded application since tasks with lower WCETs are easier to schedule and more likely to meet their deadlines.
Compiler writers in recent years have used profile information to detect the frequently executed paths in a program and there
has been considerable effort to develop compiler optimizations to improve these paths in order to reduce the average-case execution time (ACET). In this paper, we describe an approach to reduce the WCET by adapting and applying optimizations designed for frequent
paths to the worst-case (WC) paths in an application. Instead of profiling to find the frequent paths, our WCET path optimization uses feedback from
a timing analyzer to detect the WC paths in a function. Since these path-based optimizations may increase code size, the subsequent
effects on the WCET due to these optimizations are measured to ensure that the worst-case path optimizations actually improve
the WCET before committing to a code size increase. We evaluate these WC path optimizations and present results showing the
decrease in WCET versus the increase in code size.
A preliminary version of this paper entitled “Improving WCET by optimizing worst-case paths” appeared in the 2005 Real-Time and Embedded Technology and Applications Symposium.
Wankang Zhao received his PhD in Computer Science from Florida State University in 2005. He was an associate professor in Nanjin University
of Post and Telecommunications. He is currently working for Datamaxx Corporation.
William Kreahling received his PhD in Computer Science from Florida State University in 2005. He is currently an assistant professor in the
Math and Computer Science department at Western Carolina University. His research interests include compilers, computer architecture
and parallel computing.
David Whalley received his PhD in CS from the University of Virginia in 1990. He is currently the E.P. Miles professor and chair of the
Computer Science department at Florida State University. His research interests include low-level compiler optimizations,
tools for supporting the development and maintenance of compilers, program performance evaluation tools, predicting execution
time, computer architecture, and embedded systems. Some of the techniques that he developed for new compiler optimizations
and diagnostic tools are currently being applied in industrial and academic compilers. His research is currently supported
by the National Science Foundation. More information about his background and research can be found on his home page, http://www.cs.fsu.edu/∼whalley.
Dr. Whalley is a member of the IEEE Computer Society and the Association for Computing Machinery.
Chris Healy earned a PhD in computer science from Florida State University in 1999, and is currently an associate professor of computer
science at Furman University. His research interests include static and parametric timing analysis, real-time and embedded
systems, compilers and computer architecture. He is committed to research experiences for undergraduate students, and his
work has been supported by funding from the National Science Foundation. He is a member of ACM and the IEEE Computer Society.
Frank Mueller is an Associate Professor in Computer Science and a member of the Centers for Embedded Systems Research (CESR) and High Performance
Simulations (CHiPS) at North Carolina State University. Previously, he held positions at Lawrence Livermore National Laboratory
and Humboldt University Berlin, Germany. He received his Ph.D. from Florida State University in 1994. He has published papers
in the areas of embedded and real-time systems, compilers and parallel and distributed systems. He is a founding member of
the ACM SIGBED board and the steering committee chair of the ACM SIGPLAN LCTES conference. He is a member of the ACM, ACM
SIGPLAN, ACM SIGBED and the IEEE Computer Society. He is a recipient of an NSF Career Award. 相似文献
8.
In some business applications such as trading management in financial institutions, it is required to accurately answer ad
hoc aggregate queries over data streams. Materializing and incrementally maintaining a full data cube or even its compression
or approximation over a data stream is often computationally prohibitive. On the other hand, although previous studies proposed
approximate methods for continuous aggregate queries, they cannot provide accurate answers. In this paper, we develop a novel
prefix aggregate tree (PAT) structure for online warehousing data streams and answering ad hoc aggregate queries. Often, a data stream can be partitioned
into the historical segment, which is stored in a traditional data warehouse, and the transient segment, which can be stored in a PAT to answer ad hoc aggregate queries. The size of a PAT is linear in the size of the transient
segment, and only one scan
of the data stream is needed to create and incrementally maintain a PAT. Although the query answering using PAT costs more
than the case of a fully materialized data cube, the query answering time is still kept linear in the size of the transient
segment. Our extensive experimental results on both synthetic and real data sets illustrate the efficiency and the scalability
of our design.
Moonjung Cho is a Ph.D. candidate in the Department of Computer Science and Engineering at State University of New York at Buffalo. She
obtained her Master from same university in 2003. She has industry experiences as associate researcher for 4 years. Her research
interests are in the area of data mining, data warehousing and data cubing. She has received a full scholarship from Institute
of Information Technology Assessment in Korea.
Jian Pei received the Ph.D. degree in Computing Science from Simon Fraser University, Canada, in 2002. He is currently an Assistant
Professor of Computing Science at Simon Fraser University, Canada. In 2002–2004, he was an Assistant Professor of Computer
Science and Engineering at the State University of New York at Buffalo, USA. His research interests can be summarized as developing
advanced data analysis techniques for emerging applications. Particularly, he is currently interested in various techniques
of data mining, data warehousing, online analytical processing, and database systems, as well as their applications in bioinformatics.
His current research is supported in part by Natural Sciences and Engineering Research Council of Canada (NSERC) and National
Science Foundation (NSF). He has published over 70 papers in refereed journals, conferences, and workshops, has served in
the program committees of over 60
international conferences and workshops, and has been a reviewer for some leading academic journals. He is a member of the
ACM, the ACM SIGMOD, and the ACM SIGKDD.
Ke Wang received Ph.D from Georgia Institute of Technology. He is currently a professor at School of Computing Science, Simon Fraser
University. Before joining Simon Fraser, he was an associate professor at National University of Singapore. He has taught
in the areas of database and data mining. Ke Wang's research interests include database technology, data mining and knowledge
discovery, machine learning, and emerging applications, with recent interests focusing on the end use of data mining. This
includes explicitly modeling the business goal (such as profit mining, bio-mining and web mining) and exploiting user prior
knowledge (such as extracting unexpected patterns and actionable knowledge). He is interested in combining the strengths of
various fields such as database, statistics, machine learning and optimization to provide actionable solutions to real life
problems. Ke Wang has published in database, information retrieval, and data mining conferences,
including SIGMOD, SIGIR, PODS, VLDB, ICDE, EDBT, SIGKDD, SDM and ICDM. He is an associate editor of the IEEE TKDE journal
and has served program committees for international conferences including DASFAA, ICDE, ICDM, PAKDD, PKDD, SIGKDD and VLDB. 相似文献
9.
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
10.
Many of today’s complex computer applications are being modeled and constructed using the principles inherent to real-time
distributed object systems. In response to this demand, the Object Management Group’s (OMG) Real-Time Special Interest Group
(RT SIG) has worked to extend the Common Object Request Broker Architecture (CORBA) standard to include real-time specifications.
This group’s most recent efforts focus on the requirements of dynamic distributed real-time systems. One open problem in this
area is resource access synchronization for tasks employing dynamic priority scheduling.
This paper presents two resource synchronization protocols that meet the requirements of dynamic distributed real-time systems
as specified by Dynamic Scheduling Real-Time CORBA 2.0 (DSRT CORBA). The proposed protocols can be applied to both Earliest
Deadline First (EDF) and Least Laxity First (LLF) dynamic scheduling algorithms, allow distributed nested critical sections,
and avoid unnecessary runtime overhead. These protocols are based on (i) distributed resource preclaiming that allocates resources
in the message-based distributed system for deadlock prevention, (ii) distributed priority inheritance that bounds local and
remote priority inversion, and (iii) distributed preemption ceilings that delimit the priority inversion time further.
Chen Zhang is an Assistant Professor of Computer Information Systems at Bryant University. He received his M.S. and Ph.D. in Computer
Science from the University of Alabama in 2000 and 2002, a B.S. from Tsinghua University, Beijing, China. Dr. Zhang’s primary
research interests fall into the areas of distributed systems and telecommunications. He is a member of ACM, IEEE and DSI.
David Cordes is a Professor of Computer Science at the University of Alabama; he has also served as Department Head since 1997. He received
his Ph.D. in Computer Science from Louisiana State University in 1988, an M.S. in Computer Science from Purdue University
in 1984, and a B.S. in Computer Science from the University of Arkansas in 1982. Dr. Cordes’s primary research interests fall
into the areas of software engineering and systems. He is a member of ACM and a Senior Member of IEEE. 相似文献