Surajit Chaudhuri, Vivek Narasayya, and Sunita Sarawagi
Modern relational database systems are beginning to support ad hoc queries on mining models. In this paper, we explore novel techniques for optimizing queries that apply mining models to relational data. For such queries, we use the internal structure of the mining model to automatically derive traditional database predicates. We present algorithms for deriving such predicates for some popular discrete mining models: decision trees, naive Bayes, and clustering. Our experiments on Microsoft SQL Server 2000 demonstrate that these derived predicates can significantly reduce the cost of evaluating such queries.
|Published in||18th International Conference on Data Engineering|
|Publisher||IEEE Computer Society|
Copyright © 2007 IEEE. Reprinted from IEEE Computer Society. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to email@example.com. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.