Abstract
Today’s business applications demand high flexibility in processing information and extracting knowledge from data. Thus, data mining becomes more and more an integral part of operating a business. However, the integration of data mining into business processes still requires a lot of coordination and manual adjustment. This paper aims at reducing this effort by reusing successful data mining solutions. We describe a novel approach on facilitating the integration based on process patterns for data mining and demonstrate that these patterns allow for easy reuse and can significantly speed up the process of integration. We empirically evaluate our approach in a case study of fraud detection in the health care domain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hornick, M.F., Marcadé, E., Venkayala, S.: Java Data Mining: Strategy, Standard, and Practice. Morgan Kaufmann, San Francisco (2006)
Wegener, D., Rüping, S.: On Integrating Data Mining into Business Processes. In: Abramowicz, W., Tolksdorf, R. (eds.) BIS 2010. LNBIP, vol. 47, pp. 183–194. Springer, Heidelberg (2010)
Shearer, C.: The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing 5(4), 13–22 (2000)
Rupnik, R., Jaklič, J.: The Deployment of Data Mining into Operational Business Processes. In: Ponce, J., Karahoca, A. (eds.) Data Mining and Knowledge Discovery in Real Life Applications, I-Tech, Vienna, Austria (2009)
Sharma, S., Osei-Bryson, K.: Framework for formal implementation of the business understanding phase of data mining projects. Expert Systems with Applications 36(2) (2009)
Marbán, O., Segovia, J., Menasalvas, E., Fernández-Baizán, C.: Toward data mining engineering: A software engineering approach. Information Systems 34(1) (2009)
Jordan, D., Evdemon, J.: Web Services Business Process Execution Language Version 2.0. Technical report, OASIS Standard (2007)
White, S.A., Miers, D.: BPMN Modeling and Reference Guide Understanding and Using BPMN. Future Strategies Inc., Lighthouse Pt (2008)
Bremer, P.: Erstellung einer Datenbasis von Workflowreihen aus realen Anwendungen, Diploma Thesis, University of Bonn (2010) (in german)
White, S.: Process Modeling Notations and Workflow Patterns. In: Fischer, L. (ed.) The Workflow Handbook 2004. Future Strategies Inc., Lighthouse Point (2004)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery in Databases. AI Magazine 17, 37–54 (1996)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Russell, N., ter Hofstede, A.H.M., van der Aalst, A.H.M., Mulyar, N.: Workflow Control-Flow Patterns: A Revised View. BPM Center Report BPM-06-22, BPMcenter.org (2006)
Atwood, D.: BPM Process Patterns: Repeatable Design for BPM Process Models. BPTrends (May 2006)
Tsai, C., Tsai, M.: A Dynamic Web Service based Data Mining Process System. In: Proc. of the Fifth International Conference on Computer and Information Technology CIT, pp. 1033–1039. IEEE Computer Society, Washington (2005)
Altintas, I., Birnbaum, A., Baldridge, K., Sudholt, W., Miller, M.A., Amoreira, C., Potier, Y., Ludscher, B.: A Framework for the Design and Reuse of Grid Workflows. In: Herrero, P., S. Pérez, M., Robles, V. (eds.) SAG 2004. LNCS, vol. 3458, pp. 120–133. Springer, Heidelberg (2005)
iWebCare Project Deliverable D01 – Business process model of e-gov fraud detection processes in the health care domain (2006), http://iwebcare.iisa-innov.com/documents/D1-BusinessProcessModelingv4.3.zip
Rüping, S., Punko, N., Günter, B., Grosskreutz, H.: Procurement Fraud Discovery using Similarity Measure Learning. Transactions on Case-based Reasoning 1(1), 37–46 (2008)
Hilario, M., Kalousis, A., Nguyen, P., Woznica, A.: A Data Mining Ontology for Algorithm Selection and Meta-Learning. In: Proc. of the ECML/PKDD 2009 Workshop on Third Generation Data Mining: Towards Service-oriented Knowledge Discovery (SoKD 2009), Bled, Slovenia, pp. 76–87 (2009)
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
Wegener, D., Rüping, S. (2011). On Reusing Data Mining in Business Processes - A Pattern-Based Approach. In: zur Muehlen, M., Su, J. (eds) Business Process Management Workshops. BPM 2010. Lecture Notes in Business Information Processing, vol 66. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20511-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-20511-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20510-1
Online ISBN: 978-3-642-20511-8
eBook Packages: Computer ScienceComputer Science (R0)