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.
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Kerdprasop, N., Kerdprasop, K. (2007). Semantic Knowledge Integration to Support Inductive Query Optimization. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_15
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DOI: https://doi.org/10.1007/978-3-540-74553-2_15
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