article

Time prediction based on process mining

Authors:

W. M. P. van der Aalst,

M. H. Schonenberg,

M. SongAuthors Info & Claims

Information Systems, Volume 36, Issue 2

Pages 450 - 475

https://doi.org/10.1016/j.is.2010.09.001

Published: 01 April 2011 Publication History

Abstract

Process mining allows for the automated discovery of process models from event logs. These models provide insights and enable various types of model-based analysis. This paper demonstrates that the discovered process models can be extended with information to predict the completion time of running instances. There are many scenarios where it is useful to have reliable time predictions. For example, when a customer phones her insurance company for information about her insurance claim, she can be given an estimate for the remaining processing time. In order to do this, we provide a configurable approach to construct a process model, augment this model with time information learned from earlier instances, and use this to predict e.g., the completion time. To provide meaningful time predictions we use a configurable set of abstractions that allow for a good balance between ''overfitting'' and ''underfitting''. The approach has been implemented in ProM and through several experiments using real-life event logs we demonstrate its applicability.

References

[1]

van der Aalst, W.M.P., van Dongen, B.F., Günther, C.W., Mans, R.S., Alves de Medeiros, A.K., Rozinat, A., Rubin, V., Song, M., Verbeek, H.M.W. and Weijters, A.J.M.M., ProM 4.0: comprehensive support for real process analysis. In: Kleijn, J., Yakovlev, A. (Eds.), Application and Theory of Petri Nets and Other Models of Concurrency (ICATPN 2007), Lecture Notes in Computer Science, vol. 4546. Springer-Verlag, Berlin. pp. 484-494.

[2]

van der Aalst, W.M.P., Pesic, M. and Song, M., Beyond process mining: from the past to present and future. In: Pernici, B. (Ed.), Advanced Information Systems Engineering, Proceedings of the 22nd International Conference on Advanced Information Systems Engineering (CAiSE'10), Lecture Notes in Computer Science, vol. 6051. Springer-Verlag, Berlin. pp. 38-52.

[3]

van der Aalst, W.M.P., Reijers, H.A., Weijters, A.J.M.M., van Dongen, B.F., Alves de Medeiros, A.K., Song, M. and Verbeek, H.M.W., Business process mining: an industrial application. Information Systems. v32 i5. 713-732.

[4]

van der Aalst, W.M.P., Rubin, V., van Dongen, B.F., Kindler, E. and Günther, C.W., Process mining: a two-step approach to balance between underfitting and overfitting. Software and Systems Modeling. v9 i1. 87-111.

[5]

van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G. and Weijters, A.J.M.M., Workflow mining: a survey of issues and approaches. Data and Knowledge Engineering. v47 i2. 237-267.

[6]

van der Aalst, W.M.P., Weijters, A.J.M.M. and Maruster, L., Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering. v16 i9. 1128-1142.

[7]

R. Agrawal, D. Gunopulos, F. Leymann, Mining process models from workflow logs, in: Sixth International Conference on Extending Database Technology, 1998, pp. 469-483.

Digital Library

[8]

Armstrong, J.S. and Collopy, F., Error measures for generalizing about forecasting methods: empirical comparisons. International Journal of Forecasting. v8 i1. 69-80.

[9]

Cook, J.E. and Wolf, A.L., Discovering models of software processes from event-based data. ACM Transactions on Software Engineering and Methodology. v7 i3. 215-249.

[10]

R. Crooy, Predictions in information systems: a process mining perspective, Master's Thesis, Eindhoven University of Technology, Eindhoven, 2008.

[11]

Datta, A., Automating the discovery of As-Is business process models: probabilistic and algorithmic approaches. Information Systems Research. v9 i3. 275-301.

[12]

S.N. den Hertog, Case prediction in BPM systems: research to the predictability of the remaining time of individual cases, Master's Thesis, Eindhoven University of Technology, Eindhoven, 2008.

[13]

van Dongen, B.F., Crooy, R.A. and van der Aalst, W.M.P., Cycle time prediction: when will this case finally be finished?. In: Meersman, R., Tari, Z. (Eds.), Proceedings of the 16th International Conference on Cooperative Information Systems, CoopIS 2008, OTM 2008. Part I, Lecture Notes in Computer Science, vol. 5331. Springer-Verlag, Berlin. pp. 319-336.

[14]

Dumas, M., van der Aalst, W.M.P. and ter Hofstede, A.H.M., Process-aware Information Systems: Bridging People and Software Through Process Technology. 2005. Wiley & Sons.

[15]

Eder, J., Panagos, E. and Rabinovich, M., Time constraints in workflow systems. In: Jarke, M., Oberweis, A. (Eds.), Proceedings of the 11th International Conference on Advanced Information Systems Engineering (CAiSE '99), Lecture Notes in Computer Science, vol. 1626. Springer-Verlag, Berlin. pp. 286-300.

[16]

J. Eder, H. Pichler, Probabilistic calculation of execution intervals for workflows, in: Proceedings of the 12th International Symposium on Temporal Representation and Reasoning, Washington, DC, USA, IEEE Computer Society, 2005, pp. 183-185.

Digital Library

[17]

Günther, C.W. and van der Aalst, W.M.P., Fuzzy mining: adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (Eds.), International Conference on Business Process Management (BPM 2007), Lecture Notes in Computer Science, vol. 4714. Springer-Verlag, Berlin. pp. 328-343.

Digital Library

[18]

Herbst, J., A machine learning approach to workflow management. In: Proceedings of the 11th European Conference on Machine Learning, Lecture Notes in Computer Science, vol. 1810. Springer-Verlag, Berlin. pp. 183-194.

[19]

Jensen, K., Kristensen, L.M. and Wells, L., Coloured Petri nets and CPN tools for modelling and validation of concurrent systems. International Journal on Software Tools for Technology Transfer. v9 i3-4. 213-254.

[20]

Kleijnen, J. and van Groenendaal, W., Simulation: A Statistical Perspective. 1992. John Wiley and Sons, New York.

[21]

Alves de Medeiros, A.K. and Günther, C.W., Process mining: using CPN tools to create test logs for mining algorithms. In: Jensen, K. (Ed.), Proceedings of the Sixth Workshop on the Practical Use of Coloured Petri Nets and CPN Tools (CPN 2005), DAIMI, vol. 576. University of Aarhus, Aarhus, Denmark. pp. 177-190.

[22]

Nakatumba, J. and van der Aalst, W.M.P., Analyzing resource behavior using process mining. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (Eds.), BPM 2009 Workshops, Proceedings of the Fifth Workshop on Business Process Intelligence (BPI'09), Lecture Notes in Business Information Processing, vol. 43. Springer-Verlag, Berlin. pp. 69-80.

[23]

Pesic, M., Schonenberg, H. and van der Aalst, W.M.P., DECLARE: full support for loosely-structured processes. In: Spies, M., Blake, M.B. (Eds.), Proceedings of the 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), IEEE Computer Society. pp. 287-298.

[24]

Reijers, H.A., Case prediction in BPM systems: a research challenge. Journal of the Korean Institute of Industrial Engineers. v33. 1-10.

[25]

Ross, S.M., A Course in Simulation. 1990. Macmillan, New York.

[26]

Rozinat, A. and van der Aalst, W.M.P., Conformance checking of processes based on monitoring real behavior. Information Systems. v33 i1. 64-95.

[27]

Rozinat, A., Mans, R.S., Song, M. and van der Aalst, W.M.P., Discovering colored Petri nets from event logs. International Journal on Software Tools for Technology Transfer. v10 i1. 57-74.

[28]

Rozinat, A., Mans, R.S., Song, M. and van der Aalst, W.M.P., Discovering simulation models. Information Systems. v34 i3. 305-327.

[29]

Rozinat, A., Wynn, M.T., van der Aalst, W.M.P., ter Hofstede, A.H.M. and Fidge, C., Workflow simulation for operational decision support using design, historic and state information. In: Dumas, M., Reichert, M., Shan, M.C. (Eds.), International Conference on Business Process Management (BPM 2008), Lecture Notes in Computer Science, vol. 5240. Springer-Verlag, Berlin. pp. 196-211.

[30]

B. Schellekens, Cycle time prediction in Staffware. Master's Thesis, Eindhoven University of Technology, Eindhoven, 2009.

[31]

Schonenberg, H., Weber, B., van Dongen, B.F. and van der Aalst, W.M.P., Supporting flexible processes through recommendations based on history. In: Dumas, M., Reichert, M., Shan, M.C. (Eds.), International Conference on Business Process Management (BPM 2008), Lecture Notes in Computer Science, vol. 5240. Springer-Verlag, Berlin. pp. 51-66.

[32]

Schonenberg, M.H., Sidorova, N., van der Aalst, W.M.P. and van Hee, K., History-dependent stochastic Petri nets. In: Pnueli, A., Virbitskaite, I., Voronkov, A. (Eds.), Perspectives of Systems Informatics of Lecture Notes in Computer Science, vol. 5947. Springer-Verlag, Berlin. pp. 366-379.

[33]

Staffware, Staffware Process Suite Version 2-White Paper, Staffware PLC, Maidenhead, UK, 2003.

[34]

Trcka, N., Pechenizkiy, M. and van der Aalst, W.M.P., Chapter 9: process mining from educational data. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R. (Eds.), Handbook of Educational Data Mining, Data Mining and Knowledge Discovery Series, Taylor and Francis.

[35]

Verwer, S.E., de Weerdt, M.M. and Witteveen, C., Efficiently learning timed models from observations. In: Wehenkel, L., Geurts, P., Maree, R. (Eds.), Benelearn, University of Liege. pp. 75-76.

[36]

Weber, B., Wild, W. and Breu, R., CBRFlow: enabling adaptive workflow management through conversational case-based reasoning. In: Advances in Case-Based Reasoning, Lecture Notes in Computer Science, vol. 3155. Springer-Verlag, Berlin. pp. 434-448.

[37]

Weijters, A.J.M.M. and van der Aalst, W.M.P., Rediscovering workflow models from event-based data using little thumb. Integrated Computer-Aided Engineering. v10 i2. 151-162.

[38]

van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J. and Serebrenik, A., Process discovery using integer linear programming. In: van Hee, K., Valk, R. (Eds.), Proceedings of the 29th International Conference on Applications and Theory of Petri Nets (Petri Nets 2008), Lecture Notes in Computer Science, vol. 5062. Springer-Verlag, Berlin. pp. 368-387.

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Copyright © Elsevier B.V. © 2010.

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Published: 01 April 2011

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Cheng LDu LLiu CHu YFang FWard T(2024)Multi-modal fusion for business process prediction in call center scenariosInformation Fusion10.1016/j.inffus.2024.102362108:COnline publication date: 1-Aug-2024
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