Industrial, scientific, and commercial applications use information systems to trace the execution of a business process. Relevant events are registered in massive logs and process mining techniques are used to automatically discover knowledge that reveals the execution and organization of the process instances (cases). In this paper, we investigate the use of a multi-level relational frequent pattern discovery method as a means of process mining. In order to process such massive logs we resort to a Grid-based implementation of the knowledge discovery algorithm that distributes the computation on several nodes of a Grid platform. Experiments are performed on real event logs.

A Grid-based Multi-Relational Approach to Process Mining

APPICE, ANNALISA;CECI, MICHELANGELO;MALERBA, Donato
2008

Abstract

Industrial, scientific, and commercial applications use information systems to trace the execution of a business process. Relevant events are registered in massive logs and process mining techniques are used to automatically discover knowledge that reveals the execution and organization of the process instances (cases). In this paper, we investigate the use of a multi-level relational frequent pattern discovery method as a means of process mining. In order to process such massive logs we resort to a Grid-based implementation of the knowledge discovery algorithm that distributes the computation on several nodes of a Grid platform. Experiments are performed on real event logs.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/69436
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