Interfacing knowledge discovery algorithms to large database management systems

The efficient mining of large, commercially credible, databases requires a solution to at least two problems: (a) better integration between existing Knowledge Discovery algorithms and popular DBMS; (b) ability to exploit opportunities for computational speedup such as data parallelism. Both problems need to be addressed in a generic manner, since the stated requirements of end-users cover a range of data mining paradigms, DBMS, and (parallel) platforms. In this paper we present a family of generic, set-based, primitive operations for Knowledge Discovery in Databases (KDD). We show how a number of well-known KDD classification metrics, drawn from paradigms such as Bayesian classifiers, Rule-Induction/Decision Tree algorithms, Instance-Based Learning methods, and Genetic Programming, can all be computed via our generic primitives. We then show how these primitives may be mapped into SQL and, where appropriate, optimised for good performance in respect of practical factors such as client-server communication overheads. We demonstrate how our primitives can support C4.5, a widely-used rule induction system. Performance evaluation figures are presented for commercially available parallel platforms, such as the IBM SP/2