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ICMA: a new efficient algorithm for process model discovery

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Abstract

Nowadays, Business processes in organizations are supported by information systems. These systems record organizational processes outputs in the form of event logs, which contain valuable information about processes and their performance. Process mining extract knowledge from event logs. One of the most important tasks in process mining is process model discovery that uses an algorithm to build a process model from a given event log. In this research, a new model which named ICMA proposed for discovering process models. This model has three steps, pre-processing phase, body of model and post-processing phase. Imperialist Competitive Algorithm (ICA) was used for the first time as body of proposed model. Nine hundred nineteen event logs were used, those are balanced event logs, unbalanced event logs, and real-life event logs. Moreover, those event logs were studied at the 0%, 1%, 5%, 10% and 20% noise levels and the results have compared to the recent Vazquise algorithm. The research findings revealed that precision and completeness of our model is better than Vazquez model. In this paper has been shown that the ICMA model compared to the other approaches in literature method drastically improved the precision and completeness of the model. In addition, the noise problem was satisfactorily solved through data pre-processing and post-processing operations.

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Notes

  1. http://www.processmining.org/logs/start

References

  1. Agrawal R, Gunopulos D, Leymann F, Saltor F, Ramos I (1998) Mining process models from workflow logs. In: Schek H-J, Alonso G (eds) Advances in database technology — EDBT’98, vol 1377. Springer, Berlin, pp 467–483

    Google Scholar 

  2. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In: IEEE congress on paper presented at the evolutionary computation

  3. de Medeiros A (2006) Genetic process mining. Ph.D. thesis, Technische Universiteit Eindhoven

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

    Article  Google Scholar 

  5. van Dongen B, de Medeiros A, Wen L, van der Aalst W (2009) Process mining: overview and outlook of petri net discovery algorithms. In: Transactions on petri nets and other models of concurrency II, Lecture Notes in Computer Science, vol 5460. Springer, Berlin, pp 225–242

  6. Bratosin C, Sidorova N, van der Aalst W (2010) Discovering process models with genetic algorithms using sampling. In: Setchi R, Jordanov I, Howlett R, Jain L (eds) Knowledge-based and intelligent information and engineering systems, pp 41–50

    Chapter  Google Scholar 

  7. Bratosin C, Sidorova N, van der Aalst W (2010) Distributed genetic process mining. Paper presented at the Evolutionary Computation (CEC)

  8. Buijs JAM, Dongen vBB, Aalst vdWW (2012) A genetic algorithm for discovering process trees. In: 2012 IEEE congress on evolutionary computation (CEC)

  9. Buijs JAM, Dongen vBB, Aalst vdWW (2012) On the role of fitness, precision, generalization and simplicity in process discovery. In: Proc of Coop IS, vol 7565 of LNCS, Springer. pp 305–322

  10. Burattin A (2015) Heuristics miner for time interval. In: Process mining techniques in business environments, pp 85–95

    Chapter  Google Scholar 

  11. Burattin A, Sperduti A (2010) Automatic determination of parameters’ values for Heuristics Miner+ +. In: IEEE congress on evolutionary computation (CEC), pp 1–8

  12. Burattin A, Sperduti A, van der Aalst WM (2012) Heuristics miners for streaming event data, 2014. In: IEEE congress on evolutionary computation (CEC), p 1212.6383

  13. Cheng HJ, Kumar A (2015) Process mining on noisy logs—can log sanitization help to improve performance? Decis Support Syst 79:138–149

    Article  Google Scholar 

  14. Cheng HJ, Ou-Yang C, Juan YC (2015) A hybrid approach to extract business process models with high fitness and precision. Journal of Industrial and Production Engineering 32(6):351–359

    Article  Google Scholar 

  15. Chifu VR, Pop CB, Salomie I, Balla I, Paven R (2012) Hybrid particle swarm optimization method for process mining. In: Intelligent computer communication and processing (ICCP), pp 273–279

  16. Cochran WG, Cox GM (1992) Experimental designs, 2nd edn. Wiley, USA

    MATH  Google Scholar 

  17. Cook JE, Wolf AL (1998) Discovering models of software processes from event-based data. ACM Trans Softw Eng Methodol 7(3):215–249

    Article  Google Scholar 

  18. Cook JE, Wolf AL (1998) Event-based detection of concurrency. ACM SIGSOFT Software Engineering Notes 23(6):35–45

    Article  Google Scholar 

  19. Cook JE, Wolf AL (1999) Software process validation: Quantitatively measuring the correspondence of a process to a model. ACM Trans Softw Eng Methodol 8(2):147–176

    Article  Google Scholar 

  20. Gao D, Liu Q (2009) an improved simulated annealing algorithm for process mining. In: Computer supported cooperative work in design, pp 474–479

  21. Herbst J (2000) A machine learning approach to workflow management. In: 11th European conference on machine learning, pp 183–194

    Chapter  Google Scholar 

  22. Herbst J (2000) Dealing with concurrency in workflow induction. In: European concurrent engineering conference, SCS Europe

  23. Herbst J (2001) Ein induktiver Ansatz zur Akquisition und Adaption von Workflow-Modellen. Ph.D. thesis. Universitat Ulm

  24. Herbst J, Karagiannis D (1998) Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. In: Ninth Int’l workshop database and expert systems applications, pp 745–752

  25. Herbst J, Karagiannis D (1999) an inductive approach to the acquisition and adaptation of workflow models. In: Ibrahim M, Drabble B (eds) Workshop intelligent workflow and process management: the new frontier for AI in business, pp 52–57

  26. Herbst J, Karagiannis D (2000) Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. Intelligent Systems in Accounting Finance, and Management 9(2):67–92

    Article  Google Scholar 

  27. de Leoni M, Van der Aalst WMP, Dees M (2016) A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf Syst 56:235–257

    Article  Google Scholar 

  28. Mannhardt F, de Leoni M, Reijers HA, van der Aalst WM (2017) Data-driven process discovery-revealing conditional infrequent behavior from event logs. In: International Conference on Advanced Information Systems Engineering, pp 545–560

  29. Maruster L, Van der Aalst W, van den Bosch WA, Daelemans W (2001) Automated discovery of workflow models from hospital data. In: 13th belgium-netherlands conference on artificial intelligence, BNAIC, pp 183–190

  30. Maruster L, Weijters AJMM, Van der Aalst WM, Van den Bosch A (2002) Process mining: discovering direct successors in process logs. In: Fifth Int’l conference in discovery science (Discovery Science, 2002), pp 364–373

  31. Medeiros AK, Weijters AJ, Aalst WM (2007) Genetic process mining: an experimental evaluation. Data Min Knowl Discov 14(2):245–304

    Article  MathSciNet  Google Scholar 

  32. Medeiros dAAK, Dongen vBB, Aalst vdWW, Weijters AT (2004) Process mining: extending the alpha-algorithm to mine short loops. Technische Universiteit Eindhoven, Eindhoven

    Google Scholar 

  33. Suriadi S, Andrews R, ter Hofstede AHM, Wynn MT (2017) Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs. Inf Syst 64:132–150

    Article  Google Scholar 

  34. Rebuge Á, Ferreira DR (2012) Business process analysis in healthcare environments: a methodology based on process mining. Inf Syst 37(2):99–116

    Article  Google Scholar 

  35. Ross RJ (1989) Taguchi techniques for quality engineering. McGraw-Hill, New York

    Google Scholar 

  36. Song W, Liu S, Liu Q (2008) Business process mining based on simulated annealing. In: The 9th international conference for young computer scientists, 2008. ICYCS 2008, pp 725–730

  37. Vahedian Khezerlou A, Alizadeh S (2014) A new model for discovering process trees from event logs. Appl Intell 41(3):725–735

    Article  Google Scholar 

  38. Broucke V, De Weerdt J (2017) Fodina: A robust and flexible heuristic process discovery technique. Decis Support Syst 100:109–118

    Article  Google Scholar 

  39. Van der Aalst WMP, De Medeiros AA, Weijters AJMM (2005) Genetic process mining. In: International conference on application and theory of petri nets, vol 48–69, pp 368–387

  40. Van der Werf JMEM, van Dongen BF, Hurkens CAJ, Serebrenik A (2008) Process discovery using integer linear programming. LNCS 5062:368–387

    MATH  Google Scholar 

  41. Van der Aalst WMP, Weijters T, Maruster L (2004) Workflow mining: discovering process models from event logs. IEEE Trans Knowl Data Eng 16(9):1128–1142

    Article  Google Scholar 

  42. Van der Aalst WMP (2011) Process mining: discovery, conformance and enhancement of business processes. Springer, Berlin

    Book  Google Scholar 

  43. Van der Aalst WMP, De Masellis R, Di Francescomarino C, Ghidini C (2017) Learning hybrid process models from events. In: Carmona J, Engels G, Kumar A (eds) Business process management. BPM 2017. Lecture notes in computer science, vol 10445. Springer, Cham

  44. Vázquez-Barreiros B, Mucientes M, Lama M (2015) Prodigen: mining complete, precise and minimal structure process models with a genetic algorithm. Inf Sci 294:315–333

    Article  MathSciNet  Google Scholar 

  45. Weijters AJMM, Van der Aalst WMPvd (2002) Workflow mining: discovering workflow models from event-based data. In: Dousson C, Hoppner F, Quiniou R (eds) ECAI workshop knowledge discovery and spatial data, pp 78–84

  46. Weijters AJMM, Van der Aalst WMPvd, Medeiros AK (2006) Process mining with the heuristics miner algorithm. TU Eindhoven: BETA Working Paper Series

  47. Weijters A, Ribeiro J (2011) Flexible heuristics miner (FHM), CIDM. In: IEEE Symposium on Computational Intelligence and Data Mining, 2011 IEEE, pp 310–317

  48. Wen L, Van der Aalst WMPvd, Wang J, Sun J (2007) Mining process models with non-free-choice constructs. Data Min Knowl Disc 15(2):145–180

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Industrial Engineering Department, K.N. Toosi University of Technology, Tehran, Iran

    Somayeh Alizadeh & Ala Norani

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  1. Somayeh Alizadeh
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  2. Ala Norani
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Correspondence to Somayeh Alizadeh.

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Alizadeh, S., Norani, A. ICMA: a new efficient algorithm for process model discovery. Appl Intell 48, 4497–4514 (2018). https://doi.org/10.1007/s10489-018-1213-3

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