Abstract
Analysis of performance is crucial in the redesign phase of business processes where bottlenecks are identified from the average waiting and service times of activities and resources in business processes. However, such averages of waiting and service times are not readily available in most event logs that only record either the start or the completion times of events in activities. The transition times between events in such logs are the only performance features that are available. This paper proposes a novel method of estimating the average latent waiting and service times from the transition times that employs the optimization of the likelihood of the probabilistic model with expectation and maximization (EM) algorithms. Our experimental results indicated that our method could estimate the average latent waiting and service times with sufficient accuracy to enable practical applications through performance analysis.
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References
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes, vol. 136. Springer (2011)
van der Aalst, W.M.P., Pesic, M., Song, M.: Beyond process mining: from the past to present and future. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 38–52. Springer, Heidelberg (2010)
van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Information Systems 36(2), 450–475 (2011)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B 39(1), 1–38 (1977)
Dumas, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Process-Aware Information Systems: Bridging People and Software through Process Technology. Wiley (2005)
Ferreira, D.R.: Performance analysis of healthcare processes through process mining. ERCIM News 89, 18–19 (2012)
Kuo, Y.H., Leung, J.M.Y., Graham, C.A.: Simulation with data scarcity: Developing a simulation model of a hospital emergency department. In: Proceedings of the 2012 Winter Simulation Conference, pp. 1–12. IEEE (2012)
Lanz, A., Weber, B., Reichert, M.: Time patterns for process-aware information systems. Requirements Engineering 19(2), 113–141 (2014)
Rogge-Solti, A., Kasneci, G.: Temporal anomaly detection in business processes. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 234–249. Springer, Heidelberg (2014)
Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining – predicting delays in service processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 42–57. Springer, Heidelberg (2014)
Sindhgatta, R., Dasgupta, G.B., Ghose, A.: Analysis of operational data for expertise aware staffing. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 317–332. Springer, Heidelberg (2014)
Zerguini, L.: On the estimation of the response time of the business process. In: 17th UK Performance Engineering Workshop, University of Leeds (2001)
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Nogayama, T., Takahashi, H. (2015). Estimation of Average Latent Waiting and Service Times of Activities from Event Logs. In: Motahari-Nezhad, H., Recker, J., Weidlich, M. (eds) Business Process Management. BPM 2016. Lecture Notes in Computer Science(), vol 9253. Springer, Cham. https://doi.org/10.1007/978-3-319-23063-4_11
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DOI: https://doi.org/10.1007/978-3-319-23063-4_11
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