Frequent Pattern Queries with Optimized Constraint-pushing Operational Semantics

In this paper we study Pattern Discovery Query Language and optimizations in the context of a Logic-based Pattern Discovery Support Environment. i.e., a flexible discovery system with capabilities to obtain, maintain, represent, and utilize both induced and deduced knowledge. In particular, since frequency provides support to any extracted knowledge, we focus our investigation on frequent pattern queries: this kind of query is at the basis of many mining tasks, and it seems appropriate to be encapsulated in a pattern discovery system as a primitive operation. We introduce an inductive language for frequent pattern queries, which is simple enough to be highly optimized and expressive enough to cover the most of interesting queries. Then we define an optimized constraint-pushing operational semantics for our inductive language. This semantics is based on a frequent pattern mining operator which is able to exploit as much as possible the given set of constraints.