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
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
Cerone, A.: Learning and activity patterns in OSS communities and their impact on software quality. In: Proceedings of OpenCert 2011, ECEASST, vol. 48 (2012)
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
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
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
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
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)
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)
Martí-Oliet, N., Meseguer, J.: Rewriting logic: roadmap and bibliography. Theor. Comput. Sci. 285(2), 121–154 (2002)
Mukala, P.: Process models for learning patterns in FLOSS repositories. Ph.D. thesis, Department of Computer Science. University of Pisa (2015)
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
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
Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)
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)
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)
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)
van der Aalst, W.M.P., Stahl, C., Processes, M.B.: A Petri Net-Oriented Approach. The MIT Press, Cambridge (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-50230-4_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50229-8
Online ISBN: 978-3-319-50230-4
eBook Packages: Computer ScienceComputer Science (R0)