Data Mining Using Query Flocks with Views
Data Mining is the process of finding trends and patterns in large data. Association rule mining become one of the most important techniques for extracting useful information such as regularities in the historical data. Query flocks extends the concept of association rule mining with a ”generate-and-test” model for many different kind of patterns. This paper further extends the query flocks with view definitions. Also, a new data mining architecture simply compiles the query flocks from datalog to SQL. On this architecture, optimizations suitable for the extended query flocks are introduced. The prototype of the system is developed on a commercial database environment. Advantages of the new design and the extension to the query flocks, together with the optimizations, are also presented.
KeywordsAssociation Rule Total Execution Time Connection Graph View Evaluation Parameterized Query
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- 1.R. Agrawal. Fast Algorithms for Mining Association Rules. Proceedings of the 20th VLDB Conference, pages 487–499, 1994.Google Scholar
- 2.R. Agrawal, T. Imielinski, and A. Swami. Mining Assocation Rules between Sets of Items in Large Databases. Proceedings of ACM SIGMOD International Conference on Management of Data, pages 207–216, 1993.Google Scholar
- 3.R. Groth. Data Mining-Building Competitive Advantage. Prentice Hall, 1999.Google Scholar
- 5.R. Ramakrishnan. Database Management Systems. McGraw-Hill, 1997.Google Scholar
- 6.R. Srikant and R. Agrawal. Mining Generalized Association Rules. Proceedings of the 21th VLDB Conference, 1995.Google Scholar
- 7.D. Tsur, S. Nestorov, and A. Rosenthal. Integrating Data Mining with Relational DBMS: A Tightly Coupled Approach. Proceedings of 4th Workshop on Next Generation Information Technologies and Systems NGITS’99, 1999.Google Scholar
- 8.D. Tsur, J.D. Ullman, S. Abiteboul, C. Clifton, R. Motwani, S. Nestorov, and A. Rosenthal. Query ocks: A Generalization of Association-Rule Mining. Proceedings of ACM SIGMOD International Conference on Management of Data, pages 1–12, 1998.Google Scholar