Abstract
The detection of changes in event logs recording the behavior of flexible processes is especially challenging and process mining algorithms generate useless “spaghetti” models out of them. Due to this, existing approaches for change detection in event logs recording the behavior of flexible processes can only localize a change point, which is of no avail when it comes to explain when, why and how a process model changed and will change. The aim of this paper is to present a novel clustering technique laying the foundation to determine a variety of changes and to foresee changes. In order to do this, four algorithms have been developed. We report the results of evaluations on synthetic as well as real-life data demonstrating the efficiency of the approach and also its broad scope of application for event and sensor data.
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.: Process Mining - Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: A genetic algorithm for discovering process trees. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)
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). https://doi.org/10.1007/978-3-642-38697-8_17
Bose, R.J.C., Van der Aalst, W.M.: Context aware trace clustering: towards improving process mining results. In: Proceedings of the 2009 SIAM International Conference on Data Mining, pp. 401–412. SIAM (2009)
Ostovar, A., Maaradji, A., La Rosa, M., ter Hofstede, A.H.M.: Characterizing drift from event streams of business processes. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 210–228. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_14
Sun, Y., Bauer, B., Weidlich, M.: Compound trace clustering to generate accurate and simple sub-process models. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601, pp. 175–190. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69035-3_12
Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00328-8_11
Hompes, B.F.A., Buijs, J.C.A.M., van der Aalst, W.M.P., Dixit, P.M., Buurman, J.: Detecting changes in process behavior using comparative case clustering. In: Ceravolo, P., Rinderle-Ma, S. (eds.) SIMPDA 2015. LNBIP, vol. 244, pp. 54–75. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53435-0_3
Weerdt, J.D., vanden Broucke, S., Vanthienen, J., Baesens, B.: Active trace clustering for improved process discovery. IEEE Trans. Knowl. Data Eng. 25(12), 2708–2720 (2013)
Evermann, J., Thaler, T., Fettke, P.: Clustering traces using sequence alignment. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 179–190. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_15
Chatain, T., Carmona, J., van Dongen, B.: Alignment-based trace clustering. In: Mayr, H.C., Guizzardi, G., Ma, H., Pastor, O. (eds.) ER 2017. LNCS, vol. 10650, pp. 295–308. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69904-2_24
Bose, R.P.J.C., van der Aalst, W.M.P.: Trace clustering based on conserved patterns: towards achieving better process models. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 170–181. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12186-9_16
Greco, G., Guzzo, A., Pontieri, L., Sacca, D.: Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Eng. 18(8), 1010–1027 (2006)
Jung, J.-Y., Bae, J., Liu, L.: Hierarchical clustering of business process models. Int. J. Innov. Comput. Inf. Control 5, 4501–4511 (2009)
Tax, N., Genga, L., Zannone, N.: On the use of hierarchical subtrace mining for efficient local process model mining. In: SIMPDA, Volume 2016 of CEUR Workshop Proceedings, pp. 8–22. CEUR-WS.org (2017)
Maaradji, A., Dumas, M., Rosa, M.L., Ostovar, A.: Detecting sudden and gradual drifts in business processes from execution traces. IEEE Trans. Knowl. Data Eng. 29(10), 2140–2154 (2017)
Koschmider, A.: Clustering event traces by behavioral similarity. In: de Cesare, S., Frank, U. (eds.) ER 2017. LNCS, vol. 10651, pp. 36–42. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70625-2_4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Koschmider, A., Moreira, D.S.V. (2018). Change Detection in Event Logs by Clustering. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science(), vol 11229. Springer, Cham. https://doi.org/10.1007/978-3-030-02610-3_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-02610-3_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02609-7
Online ISBN: 978-3-030-02610-3
eBook Packages: Computer ScienceComputer Science (R0)