Abstract
Understanding the performance of business processes is an important part of any business process intelligence project. From historical information recorded in event logs, performance can be measured and visualized on a discovered process model. Thereby the accuracy of the measured performance, e.g., waiting time, greatly depends on (1) the availability of start and completion events for activities in the event log, i.e. transactional information, and (2) the ability to differentiate between subtle control flow aspects, e.g. concurrent and interleaved execution. Current process discovery algorithms either do not use activity life cycle information in a systematic way or cannot distinguish subtle control-flow aspects, leading to less accurate performance measurements. In this paper, we investigate the automatic discovery of process models from event logs, such that performance can be measured more accurately. We discuss ways of systematically treating life cycle information in process discovery and their implications. We introduce a process discovery technique that is able to handle life cycle data and that distinguishes concurrency and interleaving. Finally, we show that it can discover models and reliable performance information from event logs only.
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
References
van der Aalst, W.M.P., van Hee, K.M., ter Hofstede, A.H.M., Sidorova, N., Verbeek, H.M.W., Voorhoeve, M., Wynn, M.T.: Soundness of workflow nets: classification, decidability, and analysis. Formal Asp. Comput. 23(3), 333–363 (2011)
van der Aalst, W., Weijters, A., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)
Adriansyah, A.: Aligning Observed and Modeled Behavior. Ph.D. thesis, Eindhoven University of Technology (2014)
Burattin, A., Sperduti, A.: Heuristics miner for time intervals. In: ESANN (2010)
De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37, 654–676 (2012)
van Dongen, B.: BPI Challenge 2012 Dataset (2012). http://dx.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f
van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)
Günther, C., Verbeek, H.: XES v2.0 (2014). http://www.xes-standard.org/
Günther, C., Rozinat, A.: Disco: discover your processes. In: CEUR Workshop Proceedings, vol. 940, pp. 40–44. CEUR-WS.org (2012)
ter Hofstede, A.H.M., van der Aalst, W.M.P., Adams, M., Russell, N.: Modern Business Process Automation - YAWL and its Support Environment. Springer, Heidelberg (2010)
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 Workshops. LNBIP, vol. 171, pp. 66–78. Springer, Heidelberg (2014)
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)
Linz, P.: An Introduction to Formal Languages and Automata. Jones & Bartlett Learning, Burlington (2011)
Redlich, D., Molka, T., Gilani, W., Blair, G., Rashid, A.: Constructs competition miner: process control-flow discovery of BP-domain constructs. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 134–150. Springer, Heidelberg (2014)
Redlich, D., Molka, T., Gilani, W., Blair, G.S., Rashid, A.: Scalable dynamic business process discovery with the constructs competition miner. In: CEUR-WP, vol. 1293, 91–107 (2014)
Schimm, G.: Process miner - a tool for mining process schemes from event-based data. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 525–528. Springer, Heidelberg (2002)
Schimm, G.: Mining exact models of concurrent workflows. Comput. Ind. 53(3), 265–281 (2004)
Senderovich, A., Leemans, S., Harel, S., Gal, A., Mandelbaum, A., van der Aalst, W.: Discovering queues from event logs with varying levels of information. BPI (2015, accepted)
Solé, M., Carmona, J.: Process mining from a basis of state regions. In: Lilius, J., Penczek, W. (eds.) PETRI NETS 2010. LNCS, vol. 6128, pp. 226–245. Springer, Heidelberg (2010)
Wen, L., Wang, J., van der Aalst, W., Huang, B., Sun, J.: A novel approach for process mining based on event types. JIIS 32(2), 163–190 (2009)
Wolffensperger, R.: Static and Dynamic Visualization of Quality and Performance Dimensions on Process Trees. Master’s thesis, Eindhoven University of Technology (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P. (2016). Using Life Cycle Information in Process Discovery. In: Reichert, M., Reijers, H. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-319-42887-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-42887-1_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42886-4
Online ISBN: 978-3-319-42887-1
eBook Packages: Computer ScienceComputer Science (R0)