Abstract
Traditional process mining approaches learn process models assuming that processes are in steady-state. This does not comply with the flexibility and adaptation often requested for information systems and business models. In fact, these approaches should discover variations to adapt to new circumstances, which is a peculiarity that conventional change analysis based on time-series, could not provide, because the processes are complex artifacts. This problem can be handled with change-aware structured representations, such as those typically used for network data. In this paper, we propose a novel pattern-based change detection (PBCD) algorithm for discovering and characterizing changes in event logs encoded as dynamic networks. In particular, PBCDs are unsupervised change detection methods, based on observed changes in sets of patterns observed over time, which are able to simultaneously detect and characterize changes in evolving data. Experimental results, on both real and synthetic data, show the usefulness and the increased accuracy with respect to state-of-the-art solutions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004). https://doi.org/10.1109/TKDE.2004.47
Assy, N., van Dongen, B.F., van der Aalst, W.M.P.: Discovering hierarchical consolidated models from process families. Adv. Inf. Syst. Eng. - CAiSE 2017, 314–329 (2017). https://doi.org/10.1007/978-3-319-59536-8_20
Bifet, A., Gavaldà, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the Seventh SIAM International Conference on Data Mining, pp. 443–448 (2007).https://doi.org/10.1137/1.9781611972771.42
Bose, R.P.J.C., van der Aalst, W.M.P., Zliobaite, I., Pechenizkiy, M.: Handling concept drift in process mining. In: Advances Information Systems Engineering, pp. 391–405 (2011). https://doi.org/10.1007/978-3-642-21640-4_30
Bose, R.P.J.C., van der Aalst, W.M.P., Zliobaite, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Networks Learn. Syst. 25(1), 154–171 (2014). https://doi.org/10.1109/TNNLS.2013.2278313
Burattin, A.: PLG2: multiperspective process randomization with online and offline simulations. BPM Demo Track 2016, 1–6 (2016)
Ceci, M., Lanotte, P.F., Fumarola, F., Cavallo, D.P., Malerba, D.: Completion time and next activity prediction of processes using sequential pattern mining. In: Discovery Science - 17th International Conference, pp. 49–61 (2014). https://doi.org/10.1007/978-3-319-11812-3_5
Hassani, M., Siccha, S., Richter, F., Seidl, T.: Efficient process discovery from event streams using sequential pattern mining. In: IEEE Symposium on Computer Intelligence 2015, pp. 1366–1373 (2015). https://doi.org/10.1109/SSCI.2015.195
Impedovo, A., Loglisci, C., Ceci, M., Malerba, D.: Condensed representations of changes in dynamic graphs through emerging subgraph mining. Eng. Appl. Artif. Intell. 94 (2020). https://doi.org/10.1016/j.engappai.2020.103830
Impedovo, A., Ceci, M., Calders, T.: Efficient and accurate non-exhaustive pattern-based change detection in dynamic networks. In: Discovery Science - 22nd International Conference, DS 2019, Split, Croatia, 28–30 October 2019, Proceedings, pp. 396–411 (2019). https://doi.org/10.1007/978-3-030-33778-0_30
Loglisci, C., Ceci, M., Impedovo, A., Malerba, D.: Mining microscopic and macroscopic changes in network data streams. Knowl. Based Syst. 161, 294–312 (2018)
Loglisci, C., Ceci, M., Malerba, D.: Discovering evolution chains in dynamic networks. In: New Frontiers in Mining Complex Patterns - First International Workshop, NFMCP 2012, Held in Conjunction with ECML/PKDD 2012, Bristol, UK, 24 September 2012, Revised Selected Papers, pp. 185–199 (2012). https://doi.org/10.1007/978-3-642-37382-4_13
Maaradji, A., Dumas, M., Rosa, M.L., Ostovar, A.: Fast and accurate business process drift detection. In: Business Process Management - 13th International Conference, pp. 406–422 (2015). https://doi.org/10.1007/978-3-319-23063-4_27
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). https://doi.org/10.1109/TKDE.2017.2720601
Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Data-driven process discovery - revealing conditional infrequent behavior from event logs. In: Advances Information Systems Engineering - 29th International Conference, pp. 545–560 (2017). https://doi.org/10.1007/978-3-319-59536-8_34
Vieira, M.R., Bakalov, P., Tsotras, V.J.: On-line discovery of flock patterns in spatio-temporal data. In: 17th ACM International Symposium on Advances in Geographic Information Systems, pp. 286–295 (2009). https://doi.org/10.1145/1653771.1653812
Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011, part of the IEEE Symposium Series on Computational Intelligence 2011, France, pp. 310–317 (2011). https://doi.org/10.1109/CIDM.2011.5949453
Acknowledgments
We acknowledge the support of the MIUR - Ministero dell’Istruzione dell’Universitàe della Ricerca through the project “TALIsMan - Tecnologie di Assistenza personALizzata per il Miglioramento della quAlità della vitA” (Grant ID: ARS01_01116), funding scheme PON RI 2014–2020. We would also like to thank Lynn Rudd for her help in reading the manuscript.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Impedovo, A., Mignone, P., Loglisci, C., Ceci, M. (2020). Simultaneous Process Drift Detection and Characterization with Pattern-Based Change Detectors. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds) Discovery Science. DS 2020. Lecture Notes in Computer Science(), vol 12323. Springer, Cham. https://doi.org/10.1007/978-3-030-61527-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-61527-7_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61526-0
Online ISBN: 978-3-030-61527-7
eBook Packages: Computer ScienceComputer Science (R0)