Towards process-oriented tool support for knowledge discovery in databases

This paper develops a scaleable, extensible tool inherently based on the view of KDD as an interactive and iterative process, namely an information manager for effective retrieval of data and results, an execution server for efficient execution, and a process support interface for guiding the user through the process.

Abstract

Knowledge Discovery in Databases (KDD) is currently a hot topic in industry and academia. Although KDD is now widely accepted as a complex process of many different phases, the focus of research behind most emerging products is on underlying algorithms and modelling techniques. The main bottleneck for KDD applications is not the lack of techniques. The challenge is to exploit and combine existing algorithms effectively, and help the user during all phases of the KDD process. In this paper, we describe the project Citrus which addresses these practically relevant issues. Starting from a commercially available system, we develop a scaleable, extensible tool inherently based on the view of KDD as an interactive and iterative process. We sketch the main components of this system, namely an information manager for effective retrieval of data and results, an execution server for efficient execution, and a process support interface for guiding the user through the process.