PROBLEMS FOR KNOWLEDGE DISCOVERY IN DATABASES AND THEIR TREATMENT IN THE STATISTICS INTERPRETER EXPLORA

In this article we describe some goals and problems of KDD. Approaches are presented which have been implemented in the Statistics Interpreter Explora, a prototype assistant system for discovering interesting findings in recurrent datasets. We introduce patterns to identify what is interesting in data and give some examples of patterns for difference-, change-, and trend-detection. Then we summarize what must be specified to define a pattern. Besides some descriptive parts, this includes a procedural verification method. Object-oriented programming techniques can simplify the specializations of general patterns. We identify search as a constituent principle of discovery and introduce object structures as a basis to induce a graph structure on the search space. We mention several strategies for graph search and describe approaches for dealing with the aggregation, redundancy, and overlapping problems. Then we address the presentation of findings in natural language and graphical form, focusing on the methods to design good graphical presentations by knowledge-based techniques. Finally, we discuss the paradigm of an adaptive discovery assistant, including the problem of how to reuse the discovered knowledge for further discovery.