Abstract
Query optimizers in current database management systems (DBMS) often face problems such as intolerably long optimization time and/or poor optimization results when optimizing complex queries. To tackle this challenge, we present a new similarity-based optimization technique in this paper. The key idea is to identify groups of similar subqueries that often appear in a complex query and share the optimization result within each group in the query. An efficient algorithm to identify similar queries in a given query and optimize the query based on similarity is presented. Our experimental results demonstrate that the proposed technique is quite promising in optimizing complex queries in a DBMS.
Research supported by the IBM Toronto Lab and The University of Michigan.
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Tao, Y., Zhu, Q., Zuzarte, C. (2003). Exploiting Similarity of Subqueries for Complex Query Optimization. In: MaÅ™Ãk, V., Retschitzegger, W., Å tÄ›pánková, O. (eds) Database and Expert Systems Applications. DEXA 2003. Lecture Notes in Computer Science, vol 2736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45227-0_73
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DOI: https://doi.org/10.1007/978-3-540-45227-0_73
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