A data mining-based framework for grid workflow management

In this paper we investigate on the exploitation of data mining techniques to analyze data coming from the enactment of workflow-based processes in a service-oriented grid infrastructure. The extracted knowledge allows users to better comprehend the behavior of the enacted processes, and can be profitably exploited to provide advanced support to several phases in the life-cycle of workflow processes, including (re-)design, matchmaking, scheduling and performance monitoring. To this purpose, we focus on recent data mining techniques specifically aimed at enabling refined analyzes of workflow executions. Moreover, we introduce a comprehensive system architecture that supports the management of grid workflows by fully taking advantage of such mining techniques.