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Detecting Process Concept Drifts from Event Logs

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On the Move to Meaningful Internet Systems. OTM 2017 Conferences (OTM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10573))

Abstract

Traditional process discovery algorithms assume processes to be in a steady state. However, process models tend to be dynamic due to various factors, which has brought challenges such as change point detection, change localization and change process discovery. Existing techniques to identify change points are sensitive to parameters and the accuracy is not satisfactory. This paper proposes a novel approach to deal with such concept drift phenomenon. Event logs can be characterized by the relationships between activities, which motivates us to transform a log into a relation matrix. By detecting the always and never intervals in each row of the relation matrix, we obtain candidate change points for each relation. Finally, all the candidate change points are combined into an overall result. The approach is also able to localize the changes between different phases. Experiments on synthetic logs show that our approach is accurate and performs better than the state of the art in detecting sudden drift.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/DBSCAN.

  2. 2.

    https://sourceforge.net/projects/pipe2/.

  3. 3.

    https://github.com/THUBPM/process-drift-detection.

References

  1. van der Aalst, W.M.P., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). doi:10.1007/978-3-642-28108-2_19

    Chapter  Google Scholar 

  2. Accorsi, R., Stocker, T.: Discovering workflow changes with time-based trace clustering. In: Aberer, K., Damiani, E., Dillon, T. (eds.) SIMPDA 2011. LNBIP, vol. 116, pp. 154–168. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34044-4_9

    Chapter  Google Scholar 

  3. Basseville, M., Nikiforov, I.V., et al.: Detection of Abrupt Changes: Theory and Application, vol. 104. Prentice Hall, Englewood Cliffs (1993)

    Google Scholar 

  4. Jagadeesh, R.P., Bose, C., van der Aalst, W.M.P., Zliobaite, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 154–171 (2014)

    Article  Google Scholar 

  5. Carmona, J., Gavaldà, R.: Online techniques for dealing with concept drift in process mining. In: Hollmén, J., Klawonn, F., Tucker, A. (eds.) IDA 2012. LNCS, vol. 7619, pp. 90–102. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34156-4_10

    Chapter  Google Scholar 

  6. Gustafsson, F.: The marginalized likelihood ratio test for detecting abrupt changes. IEEE Trans. Autom. Control 41(1), 66–78 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  7. Kawahara, Y., Yairi, T., Machida, K.: Change-point detection in time-series data based on subspace identification. In: Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 559–564. IEEE (2007)

    Google Scholar 

  8. Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 180–191. VLDB Endowment (2004)

    Google Scholar 

  9. Lamma, E., Mello, P., Riguzzi, F., Storari, S.: Applying inductive logic programming to process mining. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS, vol. 4894, pp. 132–146. Springer, Heidelberg (2008). doi:10.1007/978-3-540-78469-2_16

    Chapter  Google Scholar 

  10. Lavielle, M., Teyssiere, G.: Detection of multiple change-points in multivariate time series. Lithuanian Math. J. 46(3), 287–306 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  11. Lung-Yut-Fong, A., Lévy-Leduc, C., Cappé, O.: Homogeneity and change-point detection tests for multivariate data using rank statistics. arXiv preprint arXiv:1107.1971 (2011)

  12. Maaradji, A., Dumas, M., La Rosa, M., Ostovar, A.: Fast and accurate business process drift detection. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 406–422. Springer, Cham (2015). doi:10.1007/978-3-319-23063-4_27

    Chapter  Google Scholar 

  13. Martjushev, J., Bose, R.P.J.C., van der Aalst, W.M.P.: Change point detection and dealing with gradual and multi-order dynamics in process mining. In: Matulevičius, R., Dumas, M. (eds.) BIR 2015. LNBIP, vol. 229, pp. 161–178. Springer, Cham (2015). doi:10.1007/978-3-319-21915-8_11

    Chapter  Google Scholar 

  14. Moskvina, V., Zhigljavsky, A.: An algorithm based on singular spectrum analysis for change-point detection. Commun. Stat.-Simul. Comput. 32(2), 319–352 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  15. 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). doi:10.1007/978-3-319-59536-8_14

    Google Scholar 

  16. Polyvyanyy, A., Weidlich, M., Conforti, R., La Rosa, M., ter Hofstede, A.H.M.: The 4C spectrum of fundamental behavioral relations for concurrent systems. In: Ciardo, G., Kindler, E. (eds.) PETRI NETS 2014. LNCS, vol. 8489, pp. 210–232. Springer, Cham (2014). doi:10.1007/978-3-319-07734-5_12

    Chapter  Google Scholar 

  17. Takeuchi, J., Yamanishi, K.: A unifying framework for detecting outliers and change points from time series. IEEE Trans. Knowl. Data Eng. 18(4), 482–492 (2006)

    Article  Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. van der Aalst, W.M.P.: The application of petri nets to workflow management. J. Circ. Syst. Comput. 8(01), 21–66 (1998)

    Article  Google Scholar 

  20. van der Aalst, W.M.P., de Medeiros, A.K.A., Weijters, A.J.M.M.: Genetic process mining. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 48–69. Springer, Heidelberg (2005). doi:10.1007/11494744_5

    Chapter  Google Scholar 

  21. Wang, S., Wen, L., Kumar, A., Wang, J., Su, J.: ExRORU: a new approach to characterize the behavioral semantics of process models (short paper). In: Debruyne, C., et al. (eds.) OTM 2016. LNCS, vol. 10033, pp. 318–326. Springer, Cham (2016)

    Chapter  Google Scholar 

  22. Weidlich, M., Mendling, J., Weske, M.: Efficient consistency measurement based on behavioral profiles of process models. IEEE Trans. Softw. Eng. 37(3), 410–429 (2011)

    Article  Google Scholar 

  23. Weijters, A.J.M.M., van der Aalst, W.M.P., Alves De Medeiros, A.K.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Technical report. WP, vol. 166, pp. 1–34 (2006)

    Google Scholar 

  24. Zha, H., Wang, J., Wen, L., Wang, C., Sun, J.: A workflow net similarity measure based on transition adjacency relations. Comput. Ind. 61(5), 463–471 (2010)

    Article  Google Scholar 

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Acknowledgement

The work was supported by the National Key Research and Development Program of China (No. 2016YFB1001101) and the NSFC projects (No. 61472207, 61325008 and 71690231), and Tsinghua TNList Lab Key Projects.

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Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, 100084, China

    Canbin Zheng, Lijie Wen & Jianmin Wang

Authors
  1. Canbin Zheng
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  2. Lijie Wen
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  3. Jianmin Wang
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Corresponding author

Correspondence to Lijie Wen .

Editor information

Editors and Affiliations

  1. University of Lorraine, Nancy, France

    Hervé Panetto

  2. Odisee University College, Brussels, Belgium

    Christophe Debruyne

  3. Télécom SudParis, Évry, France

    Walid Gaaloul

  4. Tilburg University, Tilburg, The Netherlands

    Mike Papazoglou

  5. Freie Universität Berlin and Fraunhofer FOKUS, Berlin, Germany

    Adrian Paschke

  6. Università degli Studi di Milano, Crema, Italy

    Claudio Agostino Ardagna

  7. TU Graz, Graz, Austria

    Robert Meersman

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Zheng, C., Wen, L., Wang, J. (2017). Detecting Process Concept Drifts from Event Logs. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10573. Springer, Cham. https://doi.org/10.1007/978-3-319-69462-7_33

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  • DOI: https://doi.org/10.1007/978-3-319-69462-7_33

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  • Online ISBN: 978-3-319-69462-7

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