Abstract
The outcome of a business process (e.g., duration, cost, success rate) depends significantly on how well the assigned resources perform at their respective tasks. Currently, this assignment is typically based on a static resource query that specifies the minimum requirements (e.g., role) a resource has to meet. This approach has the major downside that any resource whatsoever that meets the requirements can be retrieved, possibly selecting resources that do not perform well on the task. To address this challenge, we present and evaluate in this paper a model-based approach that uses data integration and mining techniques for selecting resources based on their likely performance for the task or sub-process at hand.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Anvik, J., Hiew, L., Murphy, G.C.: Who should fix this bug? In: Proceedings of the 28th International Conference on Software Engineering, pp. 361–370. ACM (2006)
De Koster, R.B.M., De Brito, M.P., van de Vendel, M.A.: How to organise return handling: an exploratory study with nine retailer warehouses. International Journal of Retail & Distribution Management 30(8/9), 407–421 (2002)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann (2006)
Jablonski, S., Talib, R.: Agent assignment for process management: agent performance evaluation. In: Proceedigns FIT 2009 (2009)
Leyman, F., Roller, D.: Production Workflow. Prentice-Hall, Englewood Cliffs (2000)
Ly, L., Rinderle, S., Dadam, P., Reichert, M.: Mining Staff Assignment Rules from Event-Based Data. In: Bussler, C.J., Haller, A. (eds.) BPM 2005. LNCS, vol. 3812, pp. 177–190. Springer, Heidelberg (2006)
Liu, Y., Wang, J., Yang, Y., Sun, J.: A semi-automatic approach for workflow staff assignment. Computers in Industry 59(5), 463–476 (2008)
Niedermann, F., Maier, B., Radeschütz, S., Schwarz, H., Mitschang, B.: Automated Process Decision Making based on Integrated Source Data. In: Abramowicz, W. (ed.) BIS 2011. LNBIP, vol. 87, pp. 160–171. Springer, Heidelberg (2011)
Niedermann, F., Radeschütz, S., Mitschang, B.: Deep Business Optimization: A Platform for Automated Process Optimization. In: Proceedings BPSC 2010 (2010)
Niedermann, F., Radeschütz, S., Mitschang, B.: Business Process Optimization Using Formalized Optimization Patterns. In: Abramowicz, W. (ed.) BIS 2011. LNBIP, vol. 87, pp. 123–135. Springer, Heidelberg (2011)
Quinlan, J.R.: Learning with continuous classes. In: 5th Australian Joint Conference on Artificial Intelligence (1992)
Reijers, H.A., Liman Mansar, S.: Best practices in business process redesign: an overview and qualitative evaluation of successful redesign heuristics. Omega 33(4), 283–306 (2005)
Radeschütz, S., Niedermann, F., Bischoff, W.: BIAEditor - Matching Process and Operational Data for a Business Impact Analysis. In: Proceedings EDBT (2010)
Yingbo, L., Jianmin, W., Jiaguang, S.: A machine learning approach to semi-automating workflow staff assignment. In: Proceedings of the 2007 ACM Symposium on Applied Computing (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Niedermann, F., Pavel, A., Mitschang, B. (2011). Beyond Roles: Prediction Model-Based Process Resource Management. In: Abramowicz, W., Maciaszek, L., Węcel, K. (eds) Business Information Systems Workshops. BIS 2011. Lecture Notes in Business Information Processing, vol 97. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25370-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-25370-6_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25369-0
Online ISBN: 978-3-642-25370-6
eBook Packages: Computer ScienceComputer Science (R0)