T. Weijters, W. van der Aalst, Process mining: discovering workflow models from event-based data, in: Proceedings of the 13th Belgium--The Netherlands Conference on Artificial Intelligence (BNAIC 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....4) and the implementation of this technique in the workflow mining tool Little Thumb (Section 5) In Section 6 we present our experimental results. Finally, we conclude the paper by summarizing the main results and pointing out future work. 2. Related work The idea of process mining is not new [6,8,9,10,14,15,16,17,21,22,24,25,26]. Cook and Wolf have investigated similar issues in the context of software engineering processes. In [8] they describe three methods for process discovery: one using neural networks, one using a purely algorithmic approach, and one Markovian approach. The authors consider the latter two the most ....

....information. The latter technique is similar to [6,22] and suffers from the drawback that the nature of splits and joins (i.e. AND or OR) is not discovered. Compared to existing work we focus on workflow processes with concurrent behavior, i.e. detecting concurrency is one of our prime concerns [25]. Therefore, we want to distinguish AND OR splits joins explicitly. To reach this goal we combine techniques from machine learning with WorkFlow nets (WF nets, 1] WF nets are a subset of Petri nets. Note that Petri nets provide a graphical but formal language designed for modeling concurrency. ....

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A.J.M.M. Weijters and W.M.P. van der Aalst. Process Mining: Discovering Workflow Models from Event-Based Data. In B. Krse, M. de Rijke, G. Schreiber, and M. van Someren, editors, Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC

....the focus was on identifying the dependency relations between events. In [10] a technique for discovering the underlying process from hospital data is presented, under the assumption that the workflow log does not contain any noisy data. A heuristic method that can handle noise is presented in [11]; however, in some situations, the used metric is not robust enough for discovering the complete process. In this paper, the problem of process discovery from process logs is defined as: i) for each task, find its direct successor task(s) ii) in the presence of noise and (iii) when the log is ....

....is defined as: i) for each task, find its direct successor task(s) ii) in the presence of noise and (iii) when the log is incomplete. Knowing the direct successors, a Petri net model can be constructed, but we do not address this subject in the present paper, this issue is presented elsewhere [10, 11]. It is realistic to assume that workflow logs contain noise. Different situations can lead to noisy logs, like input errors or missing information (for example, in a hospital environment, a patient started a treatment into hospital X and continues it in the hospital Y; in the workflow log of ....

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T. Weijters, W.M.P. van der Aalst. Process Mining: Discovering Workflow Models from EventBased Data. In Krse, B. et. al, (eds.): Proceedings 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC'01), 25-26 October 2001, Amsterdam, The Netherlands, pp. 283-290.

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T. Weijters, W. van der Aalst, Process mining: discovering workflow models from event-based data, in: Proceedings of the 13th Belgium--The Netherlands Conference on Artificial Intelligence (BNAIC 2001.

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