Meta-Modeling Execution Times of RapidMiner operators

Knowing the execution time of a computational model, especially when dealing with large data, is crucial in deciding whether the solution of the problem is attainable in acceptable time. In the case of data mining processes, typically both the time needed for model learning and model application could be of importance. We developed a meta-mining framework for execution time estimation of data mining algorithm built in RapidMiner. Operator execution time estimation is treated as a machine learning problem for which prediction models are built using execution times obtained by running algorithms on a set of predetermined datasets. With appropriate refitting this experimental methodology is applicable to any data mining environment. We present overall framework with modelling results for a subset of RapidMiner operators, and compare non-parametric distance measures based predictions with polynomial function fitting. Finally, integration of these models in the form of standalone RapidMiner extension is demonstrated and issues related to reliability, scalability and applicability for the overall workflow execution time modelling are discussed.