The Fourth International Conference on Information Integration and Web-based Applications and Services
[ Last Modified : 5 May, 19:00 ]  


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Tutorials Speaker


M. V. Ramakrishna
School of Computer Science and Software Engineering
Monash University

Advances in computing technologies have made multimedia databases comprising large collections of images, video and sound possible. In these applications, the queries are specified by giving the target values of the attributes. An exact matching result is seldom expected. A small set of objects whose attributes best match the query attributes, are to be returned to the user in response to his query. The result set presented is expected to be ordered on the degree of match to the query attributes. There is increasing interest in such problems by the database community such problems have come into the main stream database research.
Often visual languages are used for querying and query by example is the most common form of querying such databases. Query processing is based on similarity matching between the query attributes and the corresponding attributes of the database objects. Similarity matching is based on computation of effective distance between the attributes. High dimensional indexing structures such as R+-tree, R*-tree, SS-tree, etc., are used finding the nearest objects. From the processing point of view, we categorize queries into four classes.
1. Single example object with single feature attribute.
2. Single example object with multiple feature attribute.
3. Multiple example object with single feature attribute.
4. Multiple example object with multiple feature attribute.
We first discuss simple query processing. Each feature is indexed using high dimensional indexing structure. Algorithm such as K-nearest neighbour queries and range queries are used to retrieve the most similar images. We discuss the algorithms for processing queries of type 2, which involves evaluation of combining functions. The theoretical limitations of such evaluations as derived by Fagin will be dealt with briefly. Processing queries of type 3. and 4. are more involved. We start with motivation for such queries in multimedia databases. The new taxonomy we have developed for such queries, and the resulting processing strategies will be discussed.

Dr. Ramakrishna ( obtained M.E. from Indian Institute of Science, M.Math and Ph.D. in computer science from University of Waterloo, Canada. He is at the School of Computer Science and Software Engineering, Monash University, Melbourne. Before joining Monash University, he was at the Department of Computer Science, RMIT since 1994. Earlier he was working at the Michigan State University, USA. His present research is concerned with image and multimedia databases and file structures. He is working on modeling, querying and indexing of image databases, join algorithms, high dimensional indexing. His earlier work on hashing and file structures is well recognized. He has published over 30 technical papers in ACM TODS, IEEE TKDE, Comm. of ACM etc. journals, and ACM SIGMOD, ACM PODS, IEEE ICDE etc. Conferences. He is a Member of the Editorial Board of Journal of Database Management. He has been on program committees of VLDB Conference(1995), IEEE Data Engineering Conference(1990,91,93), International Conference on Foundations of Data Organisation(1993).


Organized by :

Bandung Institute of Technology - Indonesia

National University of Singapore - Singapore

Uthrecht University - The Netherlands

TEAM ASIA Conference Networks