Towards Data Mining Operators in Database Systems: Algebra and Implementation

The KDD process is a non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. This process comprises several steps which are invoked and parametrized in an interactive and iterative manner. A uniform framework for different kinds of patterns and operators is needed to support KDD efficiently and in an integrated way. Furthermore, because of large data sets it is necessary to scale up mining algorithms in order to achieve fast user support. One task of scaling data mining algorithms is the integration of KDD operators in database management systems. Two aspects of supporting KDD are addressed in this paper. First, a uniform framework is proposed that is based on constraint database concepts as well as interestingness values of patterns. Different operators are defined uniformly in that model. Second, DBMS-coupled implementations of selected operators for decision tree mining are discussed.