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Refinement Mining: Using Data to Sift Plausible Models

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 9946)

Abstract

Process mining techniques have been developed in the ambit of business process management to extract information from event logs consisting of activities and then produce a graphical representation of the process control flow, detect relations between components involved in the process and infer data dependencies between process activities. These process characterisations allow the analyst to discover an annotated visual representation of the conceptual model or the performance model of the process, check conformance with an a priori model to detect deviations and extend the a priori model with quantitative information such as frequencies and performance data. However, a process model yielded by process mining techniques is more similar to a representation of the process behaviour rather than an actual model of the process: it often consists of a huge number of states and interconnections between them, thus resulting in a spaghettilike net which is hard to interpret or even read.

In this paper we propose a novel technique, which we call model mining, to derive an abstract but concise and functionally structured model from event logs. Such a model is not a representation of the unfolded behaviour, but comprises, instead, a set of formal rules for generating the system behaviour. The set of rules is inferred by sifting a plausible a priori model using the event logs as a sieve until a reasonably concise model is achieved (refinement mining). We use rewriting logic as the formal framework in which to perform model mining and implement our framework using the MAUDE rewrite system. Once the final formal model is attained, it can be used, within the same rewriting logic framework, to predict future evolutions of the behaviour through simulation, to carry out further validation or to analyse properties through model checking. We illustrate our approach on a case study from the field of ecology.

Keywords

  • Formal methods
  • Model-driven approaches
  • Process mining
  • Application to ecosystem modelling

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References

  1. Basuki, T.A., Cerone, A., Barbuti, R., Maggiolo-Schettini, A., Milazzo, P., Rossi, E.: Modelling the dynamics of an Aedes albopictus population. In: Proceedings of AMCA-POP 2010, Electronic Proceedings in Theoretical Computer Science, vol. 227, pp. 37–58 (2010)

    Google Scholar 

  2. Cerone, A.: Learning and activity patterns in OSS communities and their impact on software quality. In: Proceedings of OpenCert 2011, ECEASST, vol. 48 (2012)

    Google Scholar 

  3. Cerone, A.: Process mining as a modelling tool: beyond the domain of business process management. In: Bianculli, D., Calinescu, R., Rumpe, B. (eds.) SEFM 2015. LNCS, vol. 9509, pp. 139–144. Springer, Heidelberg (2015). doi:10.1007/978-3-662-49224-6_12

    CrossRef  Google Scholar 

  4. Cerone, A.: A cognitive framework based on rewriting logic for the analysis of interactive systems. In: De Nicola, R., Kühn, E. (eds.) SEFM 2016. LNCS, vol. 9763, pp. 287–303. Springer, Heidelberg (2016). doi:10.1007/978-3-319-41591-8_20

    CrossRef  Google Scholar 

  5. Češka, M., Dannenberg, F., Kwiatkowska, M., Paoletti, N.: Precise parameter synthesis for stochastic biochemical systems. In: Mendes, P., Dada, J.O., Smallbone, K. (eds.) CMSB 2014. LNCS, vol. 8859, pp. 86–98. Springer, Heidelberg (2014). doi:10.1007/978-3-319-12982-2_7

    Google Scholar 

  6. Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Talcott, C.: The Maude 2.0 system. In: Nieuwenhuis, R. (ed.) RTA 2003. LNCS, vol. 2706, pp. 76–87. Springer, Heidelberg (2003). doi:10.1007/3-540-44881-0_7

    CrossRef  Google Scholar 

  7. Gulwani, S.: Automating string processing in spreadsheets using input-output examples. In: Notices, A.S. (ed.) Proceedings of POPL 2011, vol. 46, pp. 317–330. ACM (2011)

    Google Scholar 

  8. Koksal, A.S., Pu, Y., Srivastava, S., Bodik, R., Fisher, J., Piterman, N.: Automating string processing in spreadsheets using input-output examples. In: Notices, A.S. (ed.) Proceedings of POPL 2013, vol. 48, pp. 469–482. ACM (2013)

    Google Scholar 

  9. Martí-Oliet, N., Meseguer, J.: Rewriting logic: roadmap and bibliography. Theor. Comput. Sci. 285(2), 121–154 (2002)

    MathSciNet  CrossRef  MATH  Google Scholar 

  10. Mukala, P.: Process models for learning patterns in FLOSS repositories. Ph.D. thesis, Department of Computer Science. University of Pisa (2015)

    Google Scholar 

  11. Mukala, P., Cerone, A., Turini, F.: Mining learning processes from FLOSS mailing archives. In: Janssen, M., Mäntymäki, M., Hidders, J., Klievink, B., Lamersdorf, W., Loenen, B., Zuiderwijk, A. (eds.) I3E 2015. LNCS, vol. 9373, pp. 287–298. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25013-7_23

    CrossRef  Google Scholar 

  12. Paoletti, N., Yordanov, B., Hamadi, Y., Wintersteiger, C.M., Kugler, H.: Analyzing and synthesizing genomic logic functions. In: Biere, A., Bloem, R. (eds.) CAV 2014. LNCS, vol. 8559, pp. 343–357. Springer, Heidelberg (2014). doi:10.1007/978-3-319-08867-9_23

    Google Scholar 

  13. Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)

    CrossRef  Google Scholar 

  14. Solar-Lezama, A., Rabbah, R.M., Bodik, R., Ebcioglu, K.: Programming by sketching for bit-streaming programs. In: Proceedings of PLDI 2005, ACM SIGPLAN Notices, vol. 40, pp. 281–294. ACM (2005)

    Google Scholar 

  15. Srivastava, S., Gulwani, S., Foster, J.S.: From program verification to program synthesis. In: Notices, A.S. (ed.) Proceedings of POPL 2010, vol. 45, pp. 313–326. ACM (2010)

    Google Scholar 

  16. van der Aalst, W.M.P., de Beer, H.T., can Dongen, B.F.: Process mining, verification of properties: an approach based on temporal logic, Beta Working Paper Series WT, p. 136. Eindhoven University of Technology, Eindhoven (2005)

    Google Scholar 

  17. van der Aalst, W.M.P., Stahl, C., Processes, M.B.: A Petri Net-Oriented Approach. The MIT Press, Cambridge (2011)

    Google Scholar 

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Cerone, A. (2016). Refinement Mining: Using Data to Sift Plausible Models. In: Milazzo, P., Varró, D., Wimmer, M. (eds) Software Technologies: Applications and Foundations. STAF 2016. Lecture Notes in Computer Science(), vol 9946. Springer, Cham. https://doi.org/10.1007/978-3-319-50230-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-50230-4_3

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