Abstract
Process mining techniques rely on event logs: the extraction of a process model (discovery) takes an event log as the input, the adequacy of a process model (conformance) is checked against an event log, and the enhancement of a process model is performed by using available data in the log. Several notations and formalisms for event log representation have been proposed in the recent years to enable efficient algorithms for the aforementioned process mining problems. In this paper we show how Conditional Partial Order Graphs (CPOGs), a recently introduced formalism for compact representation of families of partial orders, can be used in the process mining field, in particular for addressing the problem of compact and easy-to-comprehend representation of event logs with data. We present algorithms for extracting both the control flow as well as the relevant data parameters from a given event log and show how CPOGs can be used for efficient and effective visualisation of the obtained results. We demonstrate that the resulting representation can be used to reveal the hidden interplay between the control and data flows of a process, thereby opening way for new process mining techniques capable of exploiting this interplay. Finally, we present open-source software support and discuss current limitations of the proposed approach.
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Notes
- 1.
This paper is an extended version of [6].
- 2.
We assume a total order on the set of event attributes.
References
van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)
The ProM framework homepage (2010). http://www.promtools.org/
Song, M., van der Aalst, W.M.P.: Supporting process mining by showing events at a glance. In: Proceedings of Annual Workshop on Information Technologies and Systems (WITS), pp. 139–145 (2007)
Mokhov, A.: Conditional partial order graphs. Ph.D. thesis, Newcastle University (2009)
Mokhov, A., Khomenko, V.: Algebra of parameterised graphs. ACM Trans. Embed. Comput. Syst. (TECS) 13(4s), 143 (2014)
Mokhov, A., Carmona, J.: Event log visualisation with conditional partial order graphs: from control flow to data. In: Proceedings of the International Workshop on Algorithms and Theories for the Analysis of Event Data, ATAED, Brussels, Belgium, 22–23 June, pp. 16–30 (2015)
The Workcraft framework homepage (2009). http://www.workcraft.org
The PGminer tool repository (2015). https://github.com/tuura/process-mining
van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE TKDE 16(9), 1128–1142 (2004)
Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: A genetic algorithm for discovering process trees. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC, Brisbane, Australia, 10–15 June, pp. 1–8 (2012)
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013)
Carmona, J., Cortadella, J., Kishinevsky, M.: New region-based algorithms for deriving bounded Petri nets. IEEE Trans. Comput. 59(3), 371–384 (2010)
Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Synthesis of Petri nets from finite partial languages. Fundam. Inf. 88(4), 437–468 (2008)
Mokhov, A., Sokolov, D., Yakovlev, A.: Adapting asynchronous circuits to operating conditions by logic parametrisation. In: IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC), pp. 17–24. IEEE (2012)
Mokhov, A., Rykunov, M., Sokolov, D., Yakovlev, A.: Design of processors with reconfigurable microarchitecture. J. Low Power Electron. Appl. 4(1), 26–43 (2014)
de Micheli, G.: Synthesis and Optimization of Digital Circuits. McGraw-Hill Higher Education, New York (1994)
Wegener, I.: The complexity of Boolean functions. Johann Wolfgang Goethe-Universitat (1987)
Ponce-de-León, H., Mokhov, A.: Building bridges between sets of partial orders. In: Dediu, A.-H., Formenti, E., Martín-Vide, C., Truthe, B. (eds.) LATA 2015. LNCS, vol. 8977, pp. 145–160. Springer, Heidelberg (2015)
Nielsen, M., Plotkin, G., Winskel, G.: Petri nets, event structures and domains, part I. Theor. Comput. Sci. 13, 85–108 (1981)
McMillan, K.: Using unfoldings to avoid the state explosion problem in the verification of asynchronous circuits. In: Proceedings of Computer Aided Verification Conference (CAV), vol. 663, p. 164 (1992)
Mokhov, A., Alekseyev, A., Yakovlev, A.: Encoding of processor instruction sets with explicit concurrency control. IET Comput. Digital Tech. 5(6), 427–439 (2011)
de Gennaro, A., Stankaitis, P., Mokhov, A.: A heuristic algorithm for deriving compact models of processor instruction sets. In: International Conference on Application of Concurrency to System Design (ACSD) (2015)
Villa, T., Kam, T., Brayton, R.K., Sangiovanni-Vincentelli, A.L.: Synthesis of Finite State Machines: Logic Optimization. Springer, New York (2012)
Solé, M., Carmona, J.: Light region-based techniques for process discovery. Fundam. Inf. 113(3–4), 343–376 (2011)
van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process discovery using integer linear programming. In: ATPN, pp. 368–387 (2008)
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)
Weijters, A.J.M.M., van der Aalst, W.M.P., Alves de Medeiros, A.K.: Process mining with the heuristics miner-algorithm. Technical Report WP 166, BETA Working Paper Series, Eindhoven University of Technology (2006)
Haar, S., Kern, C., Schwoon, S.: Computing the reveals relation in occurrence nets. Theor. Comput. Sci. 493, 66–79 (2013)
Mitchell, T.M.: Machine Learning. McGraw Hill Series in Computer Science. McGraw-Hill, New York (1997)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Poliakov, I., Sokolov, D., Mokhov, A.: Workcraft: a static data flow structure editing, visualisation and analysis tool. In: Kleijn, J., Yakovlev, A. (eds.) ICATPN 2007. LNCS, vol. 4546, pp. 505–514. Springer, Heidelberg (2007)
The Scenco tool website (2015). http://www.workcraft.org/scenco
The Weka tool website (2015). http://www.cs.waikato.ac.nz/ml/weka
Marlow, S., et al.: Haskell 2010 language report (2010). http://www.haskell.org/
ActiTraC: Active Trace Clustering (2014). http://www.processmining.be/actitrac/
Dumas, M., García-Bañuelos, L.: Process mining reloaded: event structures as a unified representation of process models and event logs. In: Devillers, R., Valmari, A. (eds.) PETRI NETS 2015. LNCS, vol. 9115, pp. 33–48. Springer, Heidelberg (2015)
Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P.: Process diagnostics using trace alignment: opportunities, issues, and challenges. Inf. Syst. 37(2), 117–141 (2012)
Lu, X., Fahland, D., van der Aalst, W.M.P.: Conformance checking based on partially ordered event data. In: Fournier, F., Mendling, J. (eds.) BPM 2014 Workshops. LNBIP, vol. 202, pp. 75–88. Springer, Heidelberg (2015)
Lu, X., Mans, R., Fahland, D., van der Aalst, W.M.P.: Conformance checking in healthcare based on partially ordered event data. In: Proceedings of the IEEE Emerging Technology and Factory Automation, ETFA, Barcelona, Spain, 16–19 September, pp. 1–8 (2014)
Leemans, M., van der Aalst, W.M.P.: Discovery of frequent episodes in event logs. In: Ceravolo, P., Russo, B., Accorsi, R. (eds.) SIMPDA 2014. LNBIP, vol. 237, pp. 1–31. Springer, Heidelberg (2014)
Acknowledgments
The authors would like to thank Alessandro de Gennaro and Danil Sokolov for their help with the integration of the developed process mining tools into Workcraft. Many organisations supported this research work: Andrey Mokhov was supported by Royal Society Research Grant ‘Computation Alive’ and EPSRC project UNCOVER (EP/K001698/1); Josep Carmona was partially supported by funds from the Spanish Ministry for Economy and Competitiveness (MINECO) and the European Union (FEDER funds) under grant COMMAS (ref. TIN2013-46181-C2-1-R); Jonathan Beaumont is currently a PhD student sponsored by a scholarship from the School of Electrical and Electronic Engineering, Newcastle University, UK.
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Mokhov, A., Carmona, J., Beaumont, J. (2016). Mining Conditional Partial Order Graphs from Event Logs. In: Koutny, M., Desel, J., Kleijn, J. (eds) Transactions on Petri Nets and Other Models of Concurrency XI. Lecture Notes in Computer Science(), vol 9930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53401-4_6
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