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Data Mining and Knowledge Discovery

Erschienen in:

01.07.2006

A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs

verfasst von: Laura Măruşter, A. J. M. M. (TON) Weijters, Wil M. P. Van Der Aalst, Antal Van Den Bosch

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 1/2006

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Abstract

Effective information systems require the existence of explicit process models. A completely specified process design needs to be developed in order to enact a given business process. This development is time consuming and often subjective and incomplete. We propose a method that constructs the process model from process log data, by determining the relations between process tasks. To predict these relations, we employ machine learning technique to induce rule sets. These rule sets are induced from simulated process log data generated by varying process characteristics such as noise and log size. Tests reveal that the induced rule sets have a high predictive accuracy on new data. The effects of noise and imbalance of execution priorities during the discovery of the relations between process tasks are also discussed. Knowing the causal, exclusive, and parallel relations, a process model expressed in the Petri net formalism can be built. We illustrate our approach with real world data in a case study.

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For more information see http://www.processmining.org.

T ^∗ is the set of all sequences that are composed of zero or more tasks of T. W: T ^∗→ \(\mathcal{N}\) is a function from the elements of T ^∗ to N (i.e., the number of times an element of T ^∗ appears in the process log).

We use a capital letter and || when referring to the number of occurrences of some task.

The name of the organization is not given for confidentiality reasons.

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Titel: A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs
verfasst von: Laura Măruşter
A. J. M. M. (TON) Weijters
Wil M. P. Van Der Aalst
Antal Van Den Bosch
Publikationsdatum: 01.07.2006
Verlag: Springer US
Erschienen in: Data Mining and Knowledge Discovery / Ausgabe 1/2006
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-005-0029-z

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