2008

referat

angielski

EN Data mining algorithms are often embedded in more complex systems, serving as the provider of data for internal decision making within these systems. In this paper we address an interesting problem of using data mining techniques for database query optimization. We introduce the concept of conditional cardinality patterns and design an algorithm to compute the required values for a given database schema. However applicable to any database system, our solution is best suited for data warehouse environments due to the special characteristics of both database schemata being used and queries being asked. We verify our proposal experimentally by running our algorithm against the state-of-the-art database query optimizer. The results of conducted experiments show that our algorithm outperforms traditional cost-based query optimizer with respect to the accuracy of cardinality estimation for a wide range of queries.

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10.1007/978-3-540-85836-2_14

https://link.springer.com/chapter/10.1007/978-3-540-85836-2_14

Data Warehousing and Knowledge Discovery : 10th International Conference, DaWaK 2008 Turin, Italy, September 2008 : proceedings

10th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2008, 2-5.09.2008, Turin, Italy