Abstract
Analysing performance of business processes is an important vehicle to improve their operation. Specifically, an accurate assessment of sojourn times and remaining times enables bottleneck analysis and resource planning. Recently, methods to create respective performance models from event logs have been proposed. These works are severely limited, though: They either consider control-flow and performance information separately, or rely on an ad-hoc selection of temporal relations between events. In this paper, we introduce the Temporal Network Representation (TNR) of a log, based on Allen’s interval algebra, as a complete temporal representation of a log, which enables simultaneous discovery of control-flow and performance information. We demonstrate the usefulness of the TNR for detecting (unrecorded) delays and for probabilistic mining of variants when modelling the performance of a process. In order to compare different models from the performance perspective, we develop a framework for measuring performance fitness. Under this framework, we provide guarantees that TNR-based process discovery dominates existing techniques in measuring performance characteristics of a process. To illustrate the practical value of the TNR, we evaluate the approach against three real-life datasets. Our experiments show that the TNR yields an improvement in performance fitness over state-of-the-art algorithms.
Notes
- 1.
- 2.
Data is available at http://seeserver.iem.technion.ac.il/databases/HomeHospital/.
References
Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013)
Senderovich, A., Weidlich, M., Yedidsion, L., Gal, A., Mandelbaum, A., Kadish, S., Bunnell, C.A.: Conformance checking and performance improvement in scheduled processes: a queueing-network perspective. Inf. Syst. 62, 185–206 (2016)
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Using life cycle information in process discovery. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 204–217. Springer, Cham (2016). doi:10.1007/978-3-319-42887-1_17
Rozinat, A., Mans, R., Song, M., van der Aalst, W.M.P.: Discovering simulation models. Inf. Syst. 34(3), 305–327 (2009)
Rogge-Solti, A., Weske, M.: Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 389–403. Springer, Heidelberg (2013). doi:10.1007/978-3-642-45005-1_27
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, Cham (2014). doi:10.1007/978-3-319-07881-6_4
Wen, L., Wang, J., van der Aalst, W.M., Huang, B., Sun, J.: A novel approach for process mining based on event types. J. Intell. Inf. Syst. 32(2), 163–190 (2009)
Allen, J.F.: Maintaining knowledge about temporal intervals. CACM 26(11), 832–843 (1983)
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38697-8_17
Alspaugh, T.A.: Software support for calculations in Allen’s interval algebra (2005)
Van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)
Weijters, A., van Der Aalst, W.M., De Medeiros, A.A.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Technical report WP 166, pp. 1–34 (2006)
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 66–78. Springer, Cham (2014). doi:10.1007/978-3-319-06257-0_6
Bickel, P.J., Doksum, K.A.: Mathematical Statistics: Basic Ideas and Selected Topics, vol. 2. CRC Press, Boca Raton (2015)
Armony, M., et al.: On patient flow in hospitals: a data-based queueing-science perspective. Stoch. Syst. 5(1), 146–194 (2015)
Burattin, A.: Heuristics miner for time interval. In: Process Mining Techniques in Business Environments: Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining. LNBIP, vol. 207, pp. 85–95. Springer, Cham (2015). doi:10.1007/978-3-319-17482-2_11
van der Aalst, W.M.P., Schonenberg, M., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)
Vila, L.: A survey on temporal reasoning in artificial intelligence. AI Commun. 7(1), 4–28 (1994)
Freksa, C.: Temporal reasoning based on semi-intervals. Artif. Intell. 54(1–2), 199–227 (1992)
Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). doi:10.1007/978-3-319-07881-6_31
Senderovich, A., Leemans, S.J.J., Harel, S., Gal, A., Mandelbaum, A., van der Aalst, W.M.P.: Discovering queues from event logs with varying levels of information. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 154–166. Springer, Cham (2016). doi:10.1007/978-3-319-42887-1_13
Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75183-0_24
Fahland, D., Van Der Aalst, W.M.: Simplifying discovered process models in a controlled manner. Inf. Syst. 38(4), 585–605 (2013)
Senderovich, A., Shleyfman, A., Weidlich, M., Gal, A., Mandelbaum, A.: P\(^3\)-Folder: optimal model simplification for improving accuracy in process performance prediction. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 418–436. Springer, Cham (2016). doi:10.1007/978-3-319-45348-4_24
Rozinat, A., van der Aalst, W.M.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)
Adriansyah, A., Munoz-Gama, J., Carmona, J., Dongen, B.F., van der Aalst, W.M.P.: Alignment based precision checking. In: Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 137–149. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36285-9_15
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Senderovich, A., Weidlich, M., Gal, A. (2017). Temporal Network Representation of Event Logs for Improved Performance Modelling in Business Processes. In: Carmona, J., Engels, G., Kumar, A. (eds) Business Process Management. BPM 2017. Lecture Notes in Computer Science(), vol 10445. Springer, Cham. https://doi.org/10.1007/978-3-319-65000-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-65000-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64999-3
Online ISBN: 978-3-319-65000-5
eBook Packages: Computer ScienceComputer Science (R0)