Rough set based computation times estimation on Knowledge Grid

Efficient estimating the application computation times of data mining is a key component of successful scheduling on Knowledge Grid. In this paper, we present a holistic approach to estimation that uses rough sets theory to determine a reduct and then compute a runtime estimate. The heuristic reduct algorithm is based on frequencies of attributes appeared in discernibility matrix. We also present to add dynamic information about the performances of various data mining tools over specific data sources to the Knowledge Grid service for supporting the estimation. This information can be added as additional metadata stored in Knowledge Metadata Repository of Grid. Experimental result validates our solution that rough sets provide a formal framework for the problem of application run times estimation in Grid environment.