Department of Computer and Information Science
College of Engineering and Computer Science
The University of Michigan - Dearborn
4901 Evergreen Road
Dearborn, MI 48128-1491
Email: cisdb [AT] engin [DOT] umd [DOT] umich [DOT] edu
The Database Research Group in the Department of Computer and Information Science at The University of Michigan - Dearborn has strong faculty members who are active in the database area. They conduct innovative research in database management systems and related areas. Their research projects have been funded by various federal and industrial sponsors including the U.S. National Science Foundation, the IBM Corporation, and the Ford Motor Company. The faculty members in the group have published research results in refereed quality journals and conference proceedings including ACM Transactions on Database Systems, ACM Transactions on Information Systems, IEEE Transactions on Data and Knowledge Engineering, IEEE Transactions on Multimedia, the VLDB Journal, Information Systems, Data and Knowledge Engineering, Distributed and Parallel Databases, Pattern Recognition, International Conference on Very Large Data Bases (VLDB), IEEE International Conference on Data Engineering (ICDE), and IEEE International Conference on Multimedia Computing and Systems (ICMCS). They actively participate in professional activities including serving on the editorial boards for various technical journals and magazines (e.g., IEEE Multimedia, Pattern Recognition, and Multimedia Tools and Applications) and serving as program/organizing committee members/chairs for numerious international conferences (e.g., ICDE, DEXA, WISE, WAIM, ACM Multimedia, etc).
The group also has a strong commitment to training students (both graduate as well as undergraduate) and preparing them for their future roles as database research scientists or database technology specialists in the field.
The current research interests of faculty members in the group include image indexing and retrieval, multidatabase management, semantic Web, data mining, query optimization, and self-managing databases.
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