Abstract
We investigate a method designed to improve accuracy of workflow mining in the case that the identification of task labels for log events are uncertain. Here we consider how the accuracy of an independent task identifier, such as a classification or clustering engine, can be improved by examining workflow. After briefly introducing the notion of iterative workflow mining, where the mined workflow is used to help improve the true task labelings which, when re-mined, will produce a more accurate workflow model, we demonstrate a Bayesian updating approach to determining posterior probabilities for each label for a given event, by considering the probabilities from the previous step as well as information as to the beliefs of the labels that can be gained by examining the workflow model. Experiments show that labeling accuracy can be increased significantly, resulting in more accurate workflow models.
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
van der Aalst, W.M.P.: Process mining and monitoring processes and services: Workshop report. In: Leymann, F., Reisig, W., Thatte, S.R., van der Aalst, W.M.P. (eds.) The Role of Business Processes in Service Oriented Architectures, number 6291 in Dagstuhl Seminar Proceedings (2006)
van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow mining: A survey of issues and approaches. Data and Knowledge Engineering 47(2), 237–267 (2003)
van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)
Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Proceedings of the 6th International Conference on Extending Database Technology, pp. 469–483 (1998)
Buffett, S., Spencer, B.: A bayesian classifier for learning opponents preferences in multi-object automated negotiation. Electronic Commerce Research and Applications 6(3), 274–284 (2007)
Cook, J.E., Wolf, A.L.: Software process validation: Quantitatively measuring the correspondence of a process to a model. In: ACM Trans. Softw. Eng. Methodol, pp. 147–176 (1999)
Greco, G., Guzzo, A., Manco, G., Saccà, D.: Mining frequent instances on workflows. In: Proceedings of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 209–221 (2003)
Kushmerick, N., Lau, T.A.: Automated email activity management: An unsupervised learning approach. In: Proceedings of the 2005 International Conference on Intelligent User Interfaces, pp. 67–74 (2005)
Peterson, J.L.: Petri nets. ACM Comput. Surv. 9(3), 223–252 (1977)
Rozinat, A., van der Aalst, W.M.P.: Conformance testing: Measuring the fit and appropriateness of event logs and process models. In: Proc. of the First International Workshop on Business Process Intelligence (BPI 2005), pp. 1–12 (2005)
Silva, R., Zhang, J., Shanahan, J.G.: Probabilistic workflow mining. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 275–284 (2005)
Verbeek, H.M.W., Pretorius, A.J., van der Aalst, W.M.P., van Wijk, J.J.: On petri-net synthesis and attribute-based visualization. In: Proceedings of the Workshop on Petri Nets and Software Engineering (PNSE 2007), pp. 127–142 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Her Majesty the Queen in Right of Canada
About this paper
Cite this paper
Buffett, S., Geng, L. (2009). Bayesian Classification of Events for Task Labeling Using Workflow Models. In: Ardagna, D., Mecella, M., Yang, J. (eds) Business Process Management Workshops. BPM 2008. Lecture Notes in Business Information Processing, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00328-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-00328-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00327-1
Online ISBN: 978-3-642-00328-8
eBook Packages: Computer ScienceComputer Science (R0)