Designing KDD-Workflows via HTN-Planning for Intelligent Discovery Assistance

Knowledge Discovery in Databases (KDD) has evolved a lot during the last years and reached a mature stage offering plenty of operators to solve complex data analysis tasks. However, the user support for building workflows has not progressed accordingly. The large number of operators currently available in KDD systems makes it difficult for users to successfully analyze data. In addition, the correctness of workflows is not checked before execution. Hence, the execution of a workflow frequently stops with an error after several hours of runtime. This paper presents our tools, eProPlan and eIDA, which solve the above problems by supporting the whole life-cycle of (semi-) automatic workflow generation. Our modeling tool eProPlan allows to describe operators and build a task/method decomposition grammar to specify the desired workflows. Additionally, our Intelligent Discovery Assistant, eIDA, allows to place workflows into data mining (DM) tools or workflow engines for execution.