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Mining Staff Assignment Rules from Event-Based Data

Ly, Linh Thao and Rinderle, Stefanie and Dadam, Peter and Reichert, Manfred (2005) Mining Staff Assignment Rules from Event-Based Data. In: Proc. Workshop on Business Process Intelligence (BPI) in conjunction with (BPM'05), Nancy, France.

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Official URL: http://www.springerlink.com/content/46751730t6418725/

Abstract

Process mining offers methods and techniques for capturing process behaviour from log data of past process executions. Although many promising approaches on mining the control flow have been published, no attempt has been made to mine the staff assignment situation of business processes. In this paper, we introduce the problem of mining staff assignment rules using history data and organisational information (e.g., an organisational model) as input. We show that this task can be considered an inductive learning problem and adapt a decision tree learning approach to derive staff assignment rules. In contrast to rules acquired by traditional techniques (e.g., questionnaires) the thus derived rules are objective and show the staff assignment situation at hand. Therefore, they can help to better understand the process. Moreover, the rules can be used as input for further analysis, e.g., workload balance analysis or delta analysis. This paper presents the current state of our work and points out some challenges for future research.

Item Type:Conference or Workshop Item (Paper)
Subjects:DBIS Research > Publications
ID Code:133
Deposited By: Eva Mader
BibTex Export:BibTeX
Deposited On:04 Apr 2008 11:55
Last Modified:14 Oct 2011 10:23

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