Semantic knowledge integration to support inductive query optimization

Abstract

We study query evaluation within a framework of inductive databases. An inductive database is a concept of the next generation database in that the repository should contain not only persistent and derived data, but also the patterns of stored data in a unified format. Hence, the database management system should support both data processing and data mining tasks. Having provided with a tightly-coupling environment, users can then interact with the system to create, access, and modify data as well as to induce and query mining patterns. In this paper, we present a framework and techniques of query evaluation in such an environment so that the induced patterns can play a key role as semantic knowledge in the query rewriting and optimization process. Our knowledge induction approach is based on rough set theory. We present the knowledge induction algorithm driven by a user's query and explain the method through running examples. The advantages of the proposed techniques are confirmed with experimental results. © Spnnger-Verlag Berlin Heidelberg 2007.