Abstract
The process of intelligent query answering consists of analyzing the intent of a query, rewriting the query based on the intention and other kinds of knowledge, and providing answers in an intelligent way. Producing answers effectively depends largely on users’ knowledge about the query language and the database schemas. Knowledge, either intentional or extensional, is the key ingredient of intelligence. In order to improve effectiveness and convenience of querying databases, we design a systematic way to analyze user’s request and revise the query with data mining models and materialized views. Data mining models are constrained association rules discovered from the database contents. Materialized views are pre-computed data. This paper presents the knowledge acquisition method, its implementation with the Erlang programming language, and a systematic method of rewriting query with data mining models and materialized views. We perform efficiency tests of the proposed system on a platform of deductive database using the DES system. The experimental results demonstrate the effectiveness of our system in answering queries sharing the same pattern as the available knowledge.
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Kerdprasop, N., Kerdprasop, K. (2011). Optimizing Database Queries with Materialized Views and Data Mining Models. In: Kim, Th., et al. Database Theory and Application, Bio-Science and Bio-Technology. BSBT DTA 2011 2011. Communications in Computer and Information Science, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27157-1_2
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DOI: https://doi.org/10.1007/978-3-642-27157-1_2
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