Advertisement

Towards Semantic Process Mining Through Knowledge-Based Trace Abstraction

  • G. Leonardi
  • M. Striani
  • S. Quaglini
  • A. Cavallini
  • S. MontaniEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 340)

Abstract

Many information systems nowadays record data about the process instances executed at the organization in the form of traces in a log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining.

The approach has been tested in the medical domain of stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.

Keywords

Semantic process mining Knowledge-based trace abstraction Medical applications 

References

  1. 1.
    Aguilar, E.R., Ruiz, F., García, F., Piattini, M.: Evaluation measures for business process models. In: Haddad, H. (ed.) Proceedings of the 2006 ACM Symposium on Applied Computing (SAC), Dijon, France, 23–27 April 2006, pp. 1567–1568. ACM (2006)Google Scholar
  2. 2.
    Allen, J.F.: Towards a general theory of action and time. Artif. Intell. 23, 123–154 (1984)CrossRefGoogle Scholar
  3. 3.
    Azzini, A., Braghin, C., Damiani, E., Zavatarelli, F.: Using semantic lifting for improving process mining: a data loss prevention system case study. In: Accorsi, R., Ceravolo, P., Cudré-Mauroux, P. (eds.) Proceedings of the 3rd International Symposium on Data-Driven Process Discovery and Analysis, CEUR Workshop Proceedings, vol. 1027, pp. 62–73. CEUR-WS.org (2013)Google Scholar
  4. 4.
    Azzini, A., Ceravolo, P.: Consistent process mining over big data triple stores. In: IEEE International Congress on Big Data, BigData Congress 2013, pp. 54–61. IEEE Computer Society (2013)Google Scholar
  5. 5.
    Bunke, H.: On a relation between graph edit distance and maximum common subgraph. Pattern Recogn. Lett. 18(8), 689–694 (1997)CrossRefGoogle Scholar
  6. 6.
    Casati, F., Shan, M.-C.: Semantic analysis of business process executions. In: Jensen, C.S., et al. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 287–296. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45876-X_19CrossRefGoogle Scholar
  7. 7.
    Croft, W.B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice. Alternative Etext Formats. Addison-Wesley, Boston (2010)Google Scholar
  8. 8.
    de Medeiros, A.K.A., et al.: An outlook on semantic business process mining and monitoring. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2007. LNCS, vol. 4806, pp. 1244–1255. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76890-6_52CrossRefGoogle Scholar
  9. 9.
    Alves de Medeiros, A.K., van der Aalst, W.M.P.: Process mining towards semantics. In: Dillon, T.S., Chang, E., Meersman, R., Sycara, K. (eds.) Advances in Web Semantics I. LNCS, vol. 4891, pp. 35–80. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-89784-2_3CrossRefGoogle Scholar
  10. 10.
    Alves de Medeiros, A.K., van der Aalst, W.M.P., Pedrinaci, C.: Semantic process mining tools: core building blocks. In: Golden, W., Acton, T., Conboy, K., van der Heijden, H., Tuunainen, V.K. (eds.) 16th European Conference on Information Systems, ECIS 2008, Galway, Ireland, pp. 1953–1964 (2008)Google Scholar
  11. 11.
    Dijkman, R., Dumas, M., García-Bañuelos, L.: Graph matching algorithms for business process model similarity search. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 48–63. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-03848-8_5CrossRefGoogle Scholar
  12. 12.
    Ferreira, D.R., Szimanski, F., Ralha, C.G.: Improving process models by mining mappings of low-level events to high-level activities. J. Intell. Inf. Syst. 43(2), 379–407 (2014)CrossRefGoogle Scholar
  13. 13.
    Grando, M.A., Schonenberg, M.H., van der Aalst, W.M.P.: Semantic process mining for the verification of medical recommendations. In: Traver, V., Fred, A.L.N., Filipe, J., Gamboa, H. (eds.) HEALTHINF 2011 - Proceedings of the International Conference on Health Informatics, Rome, Italy, 26–29 January 2011, pp. 5–16. SciTePress (2011)Google Scholar
  14. 14.
    Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.C.: Business process intelligence. Comput. Ind. 53(3), 321–343 (2004)CrossRefGoogle Scholar
  15. 15.
    Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-75183-0_24CrossRefGoogle Scholar
  16. 16.
    Günther, C.W., Rozinat, A., van der Aalst, W.M.P.: Activity mining by global trace segmentation. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 128–139. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12186-9_13CrossRefGoogle Scholar
  17. 17.
    Hepp, M., Leymann, F., Domingue, J., Wahler, A., Fensel, D.: Semantic business process management: a vision towards using semantic web services for business process management. In: Lau, F.C.M., Lei, H., Meng, X., Wang, M. (eds.) 2005 IEEE International Conference on e-Business Engineering (ICEBE 2005), 18–21 October 2005, Beijing, China, pp. 535–540. IEEE Computer Society (2005)Google Scholar
  18. 18.
    Hepp, M., Roman, D.: An ontology framework for semantic business process management. In: Oberweis, A., Weinhardt, C., Gimpel, H., Koschmider, A., Pankratius, V., Schnizler, B. (eds.) eOrganisation: Service-, Prozess-, Market-Engineering: 8. Internationale Tagung Wirtschaftsinformatik - Band 1, WI 2007, Karlsruhe, Germany, 28 February–2 March 2007, pp. 423–440. Universitaetsverlag Karlsruhe (2007)Google Scholar
  19. 19.
    Jareevongpiboon, W., Janecek, P.: Ontological approach to enhance results of business process mining and analysis. Bus. Process Manag. J. 19(3), 459–476 (2013)CrossRefGoogle Scholar
  20. 20.
    De Maio, M.N., Salatino, M., Aliverti, E.: Mastering JBoss Drools 6 for Developers. Packt Publishing, Birmingham (2016)Google Scholar
  21. 21.
    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: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 125–141. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45348-4_8CrossRefGoogle Scholar
  22. 22.
    Montani, S., Leonardi, G., Quaglini, S., Cavallini, A., Micieli, G.: Improving structural medical process comparison by exploiting domain knowledge and mined information. Artif. Intell. Med. 62(1), 33–45 (2014)CrossRefGoogle Scholar
  23. 23.
    Palmer, M., Wu, Z.: Verb semantics for English-Chinese translation. Mach. Transl. 10, 59–92 (1995)CrossRefGoogle Scholar
  24. 24.
    Pedrinaci, C., Domingue, J.: Towards an ontology for process monitoring and mining. In: Hepp, M., Hinkelmann, K., Karagiannis, D., Klein, R., Stojanovic, N. (eds.) Proceedings of the Workshop on Semantic Business Process and Product Lifecycle Management SBPM 2007, held in conjunction with the 3rd European Semantic Web Conference (ESWC 2007), Innsbruck, Austria, 7 June 2007, vol. 251. CEUR Workshop Proceedings (2007)Google Scholar
  25. 25.
    Pedrinaci, C., Domingue, J., Brelage, C., van Lessen, T., Karastoyanova, D., Leymann, F.: Semantic business process management: scaling up the management of business processes. In: Proceedings of the 2th IEEE International Conference on Semantic Computing (ICSC 2008), 4–7 August 2008, Santa Clara, California, USA, pp. 546–553. IEEE Computer Society (2008)Google Scholar
  26. 26.
    Sell, D., Cabral, L., Motta, E., Domingue, J., dos Santos Pacheco, R.C.: Adding semantics to business intelligence. In: 16th International Workshop on Database and Expert Systems Applications (DEXA 2005), 22–26 August 2005, Copenhagen, Denmark, pp. 543–547. IEEE Computer Society (2005)Google Scholar
  27. 27.
    Syamsiyah, A., van Dongen, B.F., van der Aalst, W.M.P.: DB-XES: enabling process discovery in the large. In: Ceravolo, P., Guetl, C., Rinderle-Ma, S. (eds.) SIMPDA 2016. LNBIP, vol. 307, pp. 53–77. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-74161-1_4CrossRefGoogle Scholar
  28. 28.
    Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.: Event abstraction for process mining using supervised learning techniques. CoRR, abs/1606.07283 (2016)Google Scholar
  29. 29.
    van der Aalst, W.: Process Mining. Data Science in Action. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49851-4CrossRefGoogle Scholar
  30. 30.
    van der Aalst, W., van Dongen, B., Herbst, J., Maruster, L., Schimm, G., Weijters, A.: Workflow mining: a survey of issues and approaches. Data Knowl. Eng. 47, 237–267 (2003)CrossRefGoogle Scholar
  31. 31.
    van der Aalst, W.M.P., de Beer, H.T., van Dongen, B.F.: Process mining and verification of properties: an approach based on temporal logic. In: Meersman, R., Tari, Z. (eds.) OTM 2005. LNCS, vol. 3760, pp. 130–147. Springer, Heidelberg (2005).  https://doi.org/10.1007/11575771_11CrossRefGoogle Scholar
  32. 32.
    van Dongen, B., Alves De Medeiros, A., Verbeek, H., Weijters, A., van der Aalst, W.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) Knowl. Mang. Integr. Elem., pp. 444–454. Springer, Berlin (2005)Google Scholar
  33. 33.
    Vanderfeesten, I., Reijers, H.A., Mendling, J., van der Aalst, W.M.P., Cardoso, J.: On a quest for good process models: the cross-connectivity metric. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 480–494. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-69534-9_36CrossRefGoogle Scholar
  34. 34.
    Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: XES, XESame, and ProM 6. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 60–75. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-17722-4_5CrossRefGoogle Scholar
  35. 35.
    Weijters, A., van der Aalst, W., Alves de Medeiros, A.: Process Mining with the Heuristic Miner Algorithm, WP 166. Eindhoven University of Technology, Eindhoven (2006)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • G. Leonardi
    • 1
  • M. Striani
    • 2
  • S. Quaglini
    • 3
  • A. Cavallini
    • 4
  • S. Montani
    • 1
    Email author
  1. 1.DISIT, Computer Science InstituteUniversità del Piemonte OrientaleAlessandriaItaly
  2. 2.Department of Computer ScienceUniversità di TorinoTurinItaly
  3. 3.Department of Electrical, Computer and Biomedical EngineeringUniversità di PaviaPaviaItaly
  4. 4.I.R.C.C.S. Fondazione “C. Mondino” - on behalf of the Stroke Unit Network (SUN) Collaborating CentersPaviaItaly

Personalised recommendations