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Mining Process Model Descriptions of Daily Life Through Event Abstraction

  • N. Tax
  • N. Sidorova
  • R. Haakma
  • W. van der Aalst
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 751)

Abstract

Methods from the area of Process Mining traditionally focus on extracting insight in business processes from event logs. In this paper we explore the potential of Process Mining to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions when applied on data from smart home environments. Events in smart home environments are recorded at the level of sensor triggers, which is too low to mine habit-related behavioral patterns. Process discovery algorithms produce then overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts. We show that abstracting the events to a higher-level interpretation can enable discovery of more precise and more comprehensible models. We present a framework to automatically abstract sensor-level events to their interpretation at the human activity level. Our framework is based on the XES IEEE standard for event logs. We use supervised learning techniques to train it on training data for which both the sensor and human activity events are known. We demonstrate our abstraction framework on three real-life smart home event logs and show that the process models that can be discovered after abstraction improve on precision as well as on F-score.

References

  1. 1.
    van der Aalst, W.M.P.: The application of petri nets to workflow management. J. Circuits Syst. Comput. 8(01), 21–66 (1998)Google Scholar
  2. 2.
    van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer (2016)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Baier, T., Mendling, J., Weske, M.: Bridging abstraction layers in process mining. Inf. Syst. 46, 123–139 (2014)Google Scholar
  5. 5.
    Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Pervasive Computing, pp. 1–17. Springer (2004)Google Scholar
  6. 6.
    Bose, R.P.J.C., van der Aalst, W.M.P.: Abstractions in process mining: a taxonomy of patterns. In: Proceedings of International Conference on Business Process Management, pp. 159–175. Springer (2009)Google Scholar
  7. 7.
    Chen, L., Nugent, C.: Ontology-based activity recognition in intelligent pervasive environments. Int. J. Web Inf. Syst. 5(4), 410–430 (2009)Google Scholar
  8. 8.
    Cohen, T., Welling, M.: Harmonic exponential families on manifolds. In: Proceedings of 32nd International Conference on Machine Learning, JMLR Workshop and Conference Proceedings, pp. 1757–1765 (2015)Google Scholar
  9. 9.
    Conforti, R., Dumas, M., García-Bañuelos, L., La Rosa, M.: BPMN miner: automated discovery of BPMN process models with hierarchical structure. Inf. Syst. 56, 284–303 (2016)Google Scholar
  10. 10.
    Damerau, F.J.: A technique for computer detection and correction of spelling errors. Commun. ACM 7(3), 171–176 (1964)Google Scholar
  11. 11.
    De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: A robust F-measure for evaluating discovered process models. In: Proceedings of Symposium Series on Computational Intelligence (SSCI), pp. 148–155. IEEE (2011)Google Scholar
  12. 12.
    van Dongen, B.F., Adriansyah, A.: Process mining: fuzzy clustering and performance visualization. In: Proceedings of International Conference on Business Process Management Workshop, pp. 158–169. Springer (2010)Google Scholar
  13. 13.
    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: International Conference on Applications and Theory of Petri Nets, pp. 444–454. Springer (2005)Google Scholar
  14. 14.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)Google Scholar
  15. 15.
    Günther, C.W., Rozinat, A., van der Aalst, W.M.P.: Activity mining by global trace segmentation. In: Proceedings of International Conference on Business Process Management Workshop, pp. 128–139. Springer (2010)Google Scholar
  16. 16.
    IEEE: IEEE standard for eXtensible Event Stream (XES) for achieving interoperability in event logs and event streams. IEEE Std 1849-2016, pp. 1–50.  https://doi.org/10.1109/IEEESTD.2016.7740858 (2016)
  17. 17.
    van Kasteren, T., Kröse, B.: Bayesian activity recognition in residence for elders. In: Proceedings of 3rd IET International Conference on Intelligent Environments, pp. 209–212. IEEE (2007)Google Scholar
  18. 18.
    van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of 10th International Conference on Ubiquitous Computing, pp. 1–9. ACM (2008)Google Scholar
  19. 19.
    Katz, S.: Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living. J. Am. Geriatrics Soc. 31(12), 721–727 (1983)Google Scholar
  20. 20.
    Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. Pervasive Comput. 9(1), 48–53 (2010)Google Scholar
  21. 21.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newslett. 12(2), 74–82 (2011)Google Scholar
  22. 22.
    Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of 18th International Conference on Machine Learning. Morgan Kaufmann (2001)Google Scholar
  23. 23.
    Leemans, S.J.J.: Robust process mining with guarantees. Ph.D. thesis, Eindhoven University of Technology (2017)Google Scholar
  24. 24.
    Leotta, F., Mecella, M., Mendling, J.: Applying process mining to smart spaces: perspectives and research challenges. In: International Conference on Advanced Information Systems Engineering, pp. 298–304. Springer International Publishing (2015)Google Scholar
  25. 25.
    Lohmann, N., Verbeek, E., Dijkman, R.: Petri net transformations for business processes—a survey. In: Transactions on Petri Nets and Other Models of Concurrency II, pp. 46–63. Springer (2009)Google Scholar
  26. 26.
    Mannhardt, F., Tax, N.: Unsupervised event abstraction using pattern abstraction and local process models. In: Proceedings of International Workshop on Business Process Modeling, Development and Support, CEUR (2017)Google Scholar
  27. 27.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: From low-level events to activities—a pattern-based approach. In: Proceedings of International Conference on Business Process Management, pp. 125–141. Springer (2016)Google Scholar
  28. 28.
    Munoz-Gama, J., Carmona, J.: A fresh look at precision in process conformance. In: Proceedings of International Conference on Business Process Management, pp. 211–226. Springer (2010)Google Scholar
  29. 29.
    Peterson, J.L.: Petri Net Theory and the Modeling of Systems. Prentice Hall PTR, Upper Saddle River, NJ, USA (1981)Google Scholar
  30. 30.
    Rabiner, L.R., Juang, B.H.: An introduction to hidden Markov models. ASSP Mag. 3(1), 4–16 (1986)Google Scholar
  31. 31.
    Riboni, D., Bettini, C.: OWL 2 modeling and reasoning with complex human activities. Pervasive Mobile Comput. 7(3), 379–395 (2011)Google Scholar
  32. 32.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)Google Scholar
  33. 33.
    Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)Google Scholar
  34. 34.
    Sztyler, T., Carmona, J., Völker, J., Stuckenschmidt, H.: Self-tracking reloaded: applying process mining to personalized health care from labeled sensor data. In: Transactions on Petri Nets and Other Models of Concurrency XI, pp. 160–180. Springer, Berlin, Heidelberg (2016)Google Scholar
  35. 35.
    Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Pervasive Computing, pp. 158–175. Springer (2004)Google Scholar
  36. 36.
    Tax, N., Alasgarov, E., Sidorova, N., Haakma, R.: On generation of time-based label refinements. In: Proceedings of the 25th International Workshop on Concurrency, Specification and Programming, CEUR, pp. 25–36 (2016a)Google Scholar
  37. 37.
    Tax, N., Sidorova, N., van der Aalst, W.M.P., Haakma, R.: Heuristic approaches for generating local process models through log projections. In: Proceedings of Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2016b)Google Scholar
  38. 38.
    Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P.: Event abstraction for process mining using supervised learning techniques. In: Proceedings of the SAI Intelligent Systems Conference, pp. 161–170. Springer (2016c)Google Scholar
  39. 39.
    Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P.: Log-based evaluation of label splits for process models. Proc. Comput. Sci. 96, 63–72 (2016d)Google Scholar
  40. 40.
    Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P.: Mining local process models. J. Innov. Digital Ecosyst. 3(2), 183–196 (2016e)Google Scholar
  41. 41.
    Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 310–317. IEEE (2011)Google Scholar
  42. 42.
    Wen, L., van der Aalst, W.M.P., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Mining Knowl. Discov. 15(2), 145–180 (2007)Google Scholar
  43. 43.
    van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P.: Avoiding over-fitting in ILP-based process discovery. In: Proceedings of International Conference on Business Process Management, pp. 163–171. Springer International Publishing (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • N. Tax
    • 1
  • N. Sidorova
    • 1
  • R. Haakma
    • 2
  • W. van der Aalst
    • 1
  1. 1.Technische Universiteit EindhovenEindhovenThe Netherlands
  2. 2.Philips ResearchEindhovenThe Netherlands

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