Predator-Miner: Ad hoc Mining of Associations Rules Within a Database Management System Wee Hyong Tok, Twee Hee Ong, Wai Lup Low, Indriyati Atmosukarto, and Stephane Bressan   View Paper (PDF)   Return to Demonstrations Abstract We present a prototype system, Predator-Miner, which extends Predator with an relational-like association rule mining operator to support data mining operations. Predator-Miner allows a user to combine association rule mining queries with SQL queries. This approach towards tight integration differs from existing techniques of using user-defined functions (UDFs), stored procedures, or re-expressing a mining query as several SQL queries in two aspects. First, by encapsulating the task of association rule mining in a relational operator, we allow association rule mining to be considered as part of the query plan, on which query optimization can be performed on the mining query holistically. Second, by integrating it as a relational operator, we can leverage on the mature field of relational database technology. We extend Predator to support a variant of DMQL, and allow SQL and DMQL to be intermixed in a query. We also demonstrate a cost-based mining query optimization framework DiSC'03 © 2003 Association for Computing Machinery