Download Business Process Management Workshops: BPM 2007 by Malu Castellanos, Jan Mendling, Barbara Weber, Ton Weijters PDF

By Malu Castellanos, Jan Mendling, Barbara Weber, Ton Weijters (auth.), Arthur ter Hofstede, Boualem Benatallah, Hye-Young Paik (eds.)

This publication constitutes the completely refereed post-workshop lawsuits of 6 internationl workshops held in Brisbane, Australia, at the side of the fifth overseas convention on company approach administration, BPM 2007, in September 2007.

The forty five revised complete papers awarded have been rigorously reviewed and chosen from greater than eighty submissions to the subsequent 6 foreign workshops: enterprise procedure Intelligence (BPI 2007), enterprise strategy layout (BPD 2007), Collaborative company procedures (CBP 2007), Process-oriented info structures in Healthcare (ProHealth 2007), Reference Modeling (RefMod 2007), and Advances in Semantics for internet prone (semantics4ws 2007).

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Read Online or Download Business Process Management Workshops: BPM 2007 International Workshops, BPI, BPD, CBP, ProHealth, RefMod, semantics4ws, Brisbane, Australia, September 24, 2007, Revised Selected Papers PDF

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Extra info for Business Process Management Workshops: BPM 2007 International Workshops, BPI, BPD, CBP, ProHealth, RefMod, semantics4ws, Brisbane, Australia, September 24, 2007, Revised Selected Papers

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3812, pp. 163–176. Springer, Heidelberg (2006) 8. : Process equivalence: Comparing two process models based on observed behavior. P. ) BPM 2006. LNCS, vol. 4102, pp. 129–144. Springer, Heidelberg (2006) 9. : Process Mining with HeuristicsMiner Algorithm. no Abstract. Since ERP systems, like SAP, support the backbone operations of companies, their transaction logs provide valuable insight into the companies’ business processes. In SAP every transaction is stored and linked to relevant documents, organizational structures and other process-relevant information.

This model is more general than necessary. mining algorithm that does not support duplicate tasks, the resulting model could look like the one in Figure 2. Note that, although this model captures (or can reproduce) the behavior in the log (cf. Table 1), it is more general than necessary because: (i) attendees can use different means of transportation to reach and leave the one-day conference, (ii) attendees can skip the whole conference after traveling by car or by train, and (iii) attendees can return to the conference after going home (cf.

We use the combination of a very robust process mining algorithm (Heuristics Miner) combined with a clustering approach focusing on over-generalization. 3 Implementation This section explains how we have answered the questions raised in Section 2 and describes the resulting plug-ins that were implemented in ProM. 1 When to Further Partition a Cluster? A cluster should be further partitioned when its mined model allows for more behavior than what is expressed by the traces in the cluster. So, in our approach, generalizations in the model are captured by selecting features (or structural patterns) that, while being in principle executable according to the model, have never been registered in the log.

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