Parallel and distributed databases, data mining and knowledge discovery

Topic 5

Description

To manage the very large amount of data today available, computer scientists are working on efficient systems, algorithms and applications that can handle and analyze very large databases. Intensive data consuming applications are running on very large databases (on data warehouses, on multimedia databases) with the task to extract information diamonds. Data mining is one of the key applications here. However, these intensive data consuming applications suffer from performance problems and single database sources. Introducing data distribution and parallel processing help to overcome resource bottlenecks and to achieve guaranteed throughput, quality of service, and system scalability. Distributed architectures supported by high performance networks and intelligent middleware offer parallel and distributed databases a great opportunity to support cost-effective everyday applications.
We especially solicit submissions for either the Experience and Application Section, or the traditional System and Research Section.
Focus

Global Chair:
Prof. David Skillicorn
School of Computing
Queen's University
Kingston - Ontario
Canada

Vice Chair:
Abdelkader Hameurlain
IRIT, Universite Paul Sabatier
Toulouse
France

Vice Chair:
Prof. Paul Watson
School of Computing Science,
University of Newcastle upon Tyne,
Newcastle upon Tyne, NE1 7RU
United Kingdom

Local Chair:
Prof. Salvatore Orlando
Department of Computer Science
University of Venice
Venice, Italy


Last modified: Fri Nov 28 19:28:42 CET 2003
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