Knowledge and Data Engineering Group
The Knowledge and Data Engineering Group (KDEG) is one of three research groups supported
by the
Department of Computer Science
at the
University of Vermont. Our mission is
to foster a friendly research environment for faculty and students interested in data-oriented problems
in computer science and related fields. Currently, our favorite research topics include
artificial intelligence, data compression, database systems, data mining, image processing, machine learning,
multiresolution analysis, and
pattern recognition.. We are also developing an integrated graduate curriculum to
support student research in these areas.
Please visit our web page frequently for news about our activities, members, and publications.
News
Advanced Courses for Spring 2002:
-
CS 295: Pattern Recognition
- CS 295/331: Advanced Topics in Database and Knowledge Base Systems
- CS 295/395: Data Mining
- EE 274: Introduction to Wavelets and Filter Banks
Seminars and Conferences:
Members
Faculty:
- Byung S. Lee,
Coordinator, and Assistant Professor of Computer Science.
Research Interests: Data management, database systems.
E-mail: bslee@cs.uvm.edu.
- Gagan Mirchandani,
Professor of Electrical and Computer Engineering and Computer Science.
Research Interests: Data compression, image processing, multi-resolution analysis,
wavelets.
E-mail: mirchand@emba.uvm.edu.
- Robert R. Snapp,
Associate Professor of Computer Science and Mathematics.
Research Interests: Pattern recognition, machine learning, neural networks,
information geometry.
E-mail: snapp@cs.uvm.edu.
- Xindong Wu,
Professor of Computer Science. Research Interests: Data mining and knowledge discovery,
knowledge-based systems, electronic commerce, software engineering.
E-mail: xwu@cs.uvm.edu.
Graduate Students:
- Li Chen, Computer Science. Thesis topic: QoS Multicast Routing and Protection Planning
in Optical Networks.
- Jiangyan He, Computer Science. Thesis topic: Scalability study of queries on scientific
simulation data.
- Songtao Jiang, Computer Science. Thesis topic: TBA.
- Ji Yu, Statistics. Thesis topic: Convergence and Metric Properties of Nearest Neighbor Classifiers.
.
Former Students:
- Ross Deming, M.S. in Electrical Engineering. Thesis:
Neural networks for selective edge detection, 1993.
- Vinod Kannoth, M.S. in Computer Science,
Thesis: Modeling the execution costs of user-defined functions
in an object-relational database management system, 2001.
- Gabriel J. Kontrovitz, M.S. in Computer Science. Thesis:
Real-time, three-dimensional, line-art rendering, 2000.
- Chaoyu Jin, M.S in Electrical Engineering. Thesis:
The dependence of the approximation error on the sample size on the sample size for
feed-forward neural networks, 1997.
- Xianguan Li, M.S. in Electrical Engineering. Thesis:
Controlling traffic signals at isolated intersections using Q-Learning, 1997.
- Alessandro M. Palau, Ph.D. in Electrical Engineering. Dissertation:
Implementing nearest neighbor classifiers, 1997.
- Tong Xu, M.S. in Electrical Engineering. Thesis:
Estimating the infinite-sample risk of k nearest neighbor classifiers, 1995.
Research Projects
- Analysis of Functional Protein Interactions in Drosophila
Flight Muscles Through the Study of Mutant Proteomes, DOE EPSCoR
Computational Biology Pilot Project Award, PI: Jim Vigoreaux
(Department of Biology), Co-PI: Xindong Wu.
Collaborators
-
Data Science Group, Lawrence Livermore National Laboratory
-
Database Group, Advanced Information Technology Research Laboratory
Recent Publications
Journals:
- Byung S. Lee, Robert R. Snapp, Ron Musick, and Terrence Critchlow,
"Metadata models for ad hoc queries on tera-byte scale scientific simulations,"
Submitted, 2001.
- Robert R. Snapp and Santosh S. Venkatesh, "Asymptotic exampansions of the
k nearest neighbor risk," Annals of Statistics, vol. 26
no. 3, pp. 850-878, 1998.
- X Wu and S Zhang, "Synthesizing High-Frequency Rules from
Different Data Sources," IEEE Transactions on Knowledge and Data
Engineering, accepted, forthcoming.
Conference proceedings:
-
Ghaleb Abdulla, Chuck Baldwin, Terence Critchlow, Roy Kamimura, Ida Lozares,
Ron Musick, Nu Ai Tang, Byung S. Lee, and Robert R. Snapp, "Approximate
Ad-hoc Query Engine for Simulation Data," Proc. the First ACM+IEEE Joint
Conference on Digital Libraries, Roanoke, Virginia, USA, June, 2001, pp. 255-256.
-
Byung S. Lee, Robert R. Snapp, Ron Musick, and Terence Critchlow, "Ad hoc
Query Support for Very Large Scientific Data: the Metadata Approach,"
Proc. the 16th Brazilian Symposium on Databases, Rio de Janeiro, Brazil, October
1-3, 2001.
-
Byung S. Lee, Robert R. Snapp, and Ron Musick, "Toward a Query Language on
Simulation Mesh Data: an Object-oriented Approach," Proc. the International
Conference on Database Systems for Advanced Applications, Hong Kong, April
2001, pp. 242-249.
- R Relue, X Wu, and H Huang, "Efficient Runtime Generation of
Association Rules," Proceedings of the 10th ACM International
Conference on Information and Knowledge Management,
Doubletree Hotel Atlanta-Buckhead,
Atlanta, Georgia, USA, November 5-10, 2001, pp. 466-473.
- Robert R. Snapp, "Local polynomial metrics for k nearest neigbhor classifiers,"
Proceedings of the 2001 Workshop on Modelling Uncertainty in Geometric Computation,
Kluwer.
Technical reports:
-
Byung S. Lee, Robert Snapp, and Ron Musick, Ad hoc Query Support for Very
Large Scientific Data: the Metadata Approach, Technical Report UCRL-JC-138481,
Lawrence Livermore National Laboratory, Livermore, California. April 2000.
Last revised: November 15, 2001
For inquiries, contact Byung
S. Lee.
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