nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Mining Process Model Descriptions of Daily Life Through Event Abstraction

verfasst von : N. Tax, N. Sidorova, R. Haakma, W. van der Aalst

Erschienen in: Intelligent Systems and Applications

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A Multi-objective Genetic Algorithm for Path Planning with Micro Aerial Vehicle Test Bed

Nächstes Kapitel A Novel Approach for Time Series Forecasting with Multiobjective Clonal Selection Optimization and Modeling

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

van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer (2016)

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)

Baier, T., Mendling, J., Weske, M.: Bridging abstraction layers in process mining. Inf. Syst. 46, 123–139 (2014)

Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Pervasive Computing, pp. 1–17. Springer (2004)

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)

Chen, L., Nugent, C.: Ontology-based activity recognition in intelligent pervasive environments. Int. J. Web Inf. Syst. 5(4), 410–430 (2009)

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)

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)

10.

Damerau, F.J.: A technique for computer detection and correction of spelling errors. Commun. ACM 7(3), 171–176 (1964)

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)

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)

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)

14.

Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)

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)

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.

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)

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)

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)

20.

Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. Pervasive Comput. 9(1), 48–53 (2010)

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)

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)

23.

Leemans, S.J.J.: Robust process mining with guarantees. Ph.D. thesis, Eindhoven University of Technology (2017)

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)

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)

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)

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)

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)

29.

Peterson, J.L.: Petri Net Theory and the Modeling of Systems. Prentice Hall PTR, Upper Saddle River, NJ, USA (1981)

30.

Rabiner, L.R., Juang, B.H.: An introduction to hidden Markov models. ASSP Mag. 3(1), 4–16 (1986)

31.

Riboni, D., Bettini, C.: OWL 2 modeling and reasoning with complex human activities. Pervasive Mobile Comput. 7(3), 379–395 (2011)

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)

33.

Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

Titel: Mining Process Model Descriptions of Daily Life Through Event Abstraction
verfasst von: N. Tax
N. Sidorova
R. Haakma
W. van der Aalst
Verlag: Springer International Publishing
Buch: Intelligent Systems and Applications
Print ISBN: 978-3-319-69265-4

Electronic ISBN: 978-3-319-69266-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-69266-1_5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"