Extracting predicates from mining models for efficient query evaluation

Modern relational database systems are beginning to support ad hoc queries on mining models. In this article, we explore novel techniques for optimizing queries that contain predicates on the results of application of 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 a large class of popular discrete mining models: decision trees, naive Bayes, clustering and linear support vector machines. Our experiments on Microsoft SQL Server demonstrate that these derived predicates can significantly reduce the cost of evaluating such queries.