Abstract
A workflow is a partial or total automation of a business process, in which a collection of activities must be executed by humans or machines, according to certain procedural rules. This paper deals with an aspect of workflows which has not so far received much attention: providing facilities for the human system administrator to monitor the actual behavior of the workflow system in order to predict the “most probable” workflow executions. In this context, we develop a data mining algorithm for identifying frequent patterns, i.e., the workflow substructures that have been scheduled more frequently by the system. Several experiments show that our algorithm outperforms the standard approaches adapted to mining frequent instances.
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Greco, G., Guzzo, A., Manco, G., Saccà, D. (2003). Mining Frequent Instances on Workflows. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_21
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DOI: https://doi.org/10.1007/3-540-36175-8_21
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