Online Analysis of Simulation Data with Stream-based Data Mining

Abstract

Discrete event simulation is an accepted instrument for investigating the dynamic behavior of complex systems and evaluating processes. Usually simulation experts conduct simulation experiments for a predetermined system specification by manually varying parameters through educated assumptions and according to a prior defined goal. As an alternative, data farming and knowledge discovery in simulation data are ongoing and popular methods in order to uncover unknown relationships and effects in the model to gain useful information about the underlying system. Those methods usually demand broad scale and data intensive experimental design, so computing time can quickly become large. As a solution to that, we extend an existing concept of knowledge discovery in simulation data with an online stream mining component to get data mining results even while experiments are still running. For this purpose, we introduce a method for using decision tree classification in combination with clustering algorithms for analyzing simulation output data that considers the flow of experiments as a data stream. A prototypical implementation proves the basic applicability of the concept and yields large possibilities for future research.