Data mining for simulation algorithm selection

While simulationists devise ever more efficient simulation algorithms for specific applications and infrastructures, the problem of automatically selecting the most appropriate one for a given problem has received little attention so far. One reason for this is the overwhelming amount of performance data that has to be analyzed for deriving suitable selection mechanisms. We address this problem with a framework for data mining on simulation performance data, which enables the evaluation of various data mining methods in this context. Such an evaluation is essential, as there is no best data mining algorithm for all kinds of simulation performance data. Once an effective data mining approach has been identified for a specific class of problems, its results can be used to select efficient algorithms for future simulation problems. This paper covers the components of the framework, the integration of external tools, and the re-formulation of the algorithm selection problem from a data mining perspective. Basic data mining strategies for algorithm selection are outlined, and a sample algorithm selection problem from Computational Biology is presented.