Abstract
Workflow Management Systems help to execute, monitor and manage work process flow and execution. These systems, as they are executing, keep a record of who does what and when (e.g. log of events). The activity of using computer software to examine these records, and deriving various structural data results is called workflow mining. The workflow mining activity, in general, needs to encompass behavioral (process/control-flow), social, informational (data-flow), and organizational perspectives; as well as other perspectives, because workflow systems are ”people systems” that must be designed, deployed, and understood within their social and organizational contexts. In this paper, we especially focus on the behavioral perspective of a structured workflow model that preserves the proper nesting and the matched pair properties. That is, this paper proposes an ICN-based mining algorithm that rediscovers a structured workflow process model. We name it σ-Algorithm, because it is incrementally amalgamating a series of temporal workcases (workflow traces) according to three types of basic merging principles conceived in this paper. Where, a temporal workcase is a temporally ordered set of activity execution event logs. We also gives an example to show that how the algorithm works with the temporal workcases.
This research was supported by the Kyonggi University Overseas Research Grant 2004.
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Kim, K., Ellis, C.A. (2007). σ-Algorithm: Structured Workflow Process Mining Through Amalgamating Temporal Workcases. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_14
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DOI: https://doi.org/10.1007/978-3-540-71701-0_14
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