Auto-experimentation of KDD workflows based on ontological planning

Abstract

One of the problems of Knowledge Discovery in Databases (KDD) is the lack of user support for solving KDD problems. Current Data Mining (DM) systems enable the user to manually design workflows but this becomes difficult when there are too many operators to choose from or the workflow's size is too large. Therefore we propose to use auto-experimentation based on ontological planning to provide the users with automatic generated workflows as well as rankings for workflows based on several criteria (execution time, accuracy, etc.). Moreover auto-experimentation will help to validate the generated workflows and to prune and reduce their number. Furthermore we will use mixed-initiative planning to allow the users to set parameters and criteria to limit the planning search space as well as to guide the planner towards better workflows. © 2010 Springer-Verlag.