Frequent Pattern Queries: Language and Optimizations

The objective of this thesis is to study data mining query optimization in a Logicbased Knowledge Discovery Support Environment. i.e, a flexible knowledge discovery system with capabilities to obtain, maintain, represent, and utilize both induced and deduced knowledge. In particular, we focus on frequent pattern queries, since this kind of query is at the basis of many mining tasks, and it seems appropriate to be encapsulated in a DBMS 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 study optimization issues, defining novel algorithms for constrained frequent pattern mining i.e., finding all itemsets included in a transaction database that satisfy a given set of constraints. Finally, all the contributions in both the linguistic part of the thesis and the algorithmic part, are amalgamated together in order to define an optimized constraint-pushing operational semantics for the proposed language.