Skip to main content
Log in

The Use of Process Mining in Business Process Simulation Model Construction

Structuring the Field

  • State of the Art
  • Published:
Business & Information Systems Engineering Aims and scope Submit manuscript

Abstract

The paper focuses on the use of process mining (PM) to support the construction of business process simulation (BPS) models. Given the useful BPS insights that are available in event logs, further research on this topic is required. To provide a solid basis for future work, this paper presents a structured overview of BPS modeling tasks and how PM can support them. As directly related research efforts are scarce, a multitude of research challenges are identified. In an effort to provide suggestions on how these challenges can be tackled, an analysis of PM literature shows that few PM algorithms are directly applicable in a BPS context. Consequently, the results presented in this paper can encourage and guide future research to fundamentally bridge the gap between PM and BPS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Aguirre S, Parra C, Alvardo J (2013) Combination of process mining and simulation techniques for business process redesign: a methodological approach. Lect Notes Bus Inf 162:24–43. doi:10.1007/978-3-642-40919-6_2

    Article  Google Scholar 

  • Baier T, Mendling J, Weske M (2014) Bridging abstraction layers in process mining. Inf Syst 46:123–139. doi:10.1007/978-3-642-40176-3_4

    Article  Google Scholar 

  • Bose RPJC, van der Aalst WMP (2009) Context aware trace clustering: towards improving process mining results. In: Proceedings of the ninth SIAM international conference on data mining, pp 401–412. doi:10.1137/1.9781611972795.35

  • Bose RPJC, van der Aalst WMP (2010) Trace clustering based on conserved patterns: towards achieving better process models. Lect Notes Bus Inf 43:170–181. doi:10.1007/978-3-642-12186-9_16

    Article  Google Scholar 

  • Burattin A, Sperduti A, Veluscek M (2013) Business models enhancement through discovery of roles. In: Proceedings of the 2013 IEEE symposium on computational intelligence and data mining, pp 103–110. doi:10.1109/CIDM.2013.6597224

  • Chung AC (2004) Simulation modeling handbook: a practical approach. CRC Press, Boca Raton

    Google Scholar 

  • de Leoni M, Dumas M, García-Bañuelos L (2013) Discovering branching conditions from business process execution logs. Lect Notes Comput Sci 7793:114–129. doi:10.1007/978-3-642-37057-1_9

    Article  Google Scholar 

  • de Medeiros AKA, Guzzo A, Greco G, van der Aalst WMP, Weijters AJMM, Van Dongen BF, Saccà D (2008) Process mining based on clustering: a quest for precision. Lect Notes Comput Sci 4928:17–29. doi:10.1007/978-3-540-78238-4_4

    Article  Google Scholar 

  • De Weerdt J, De Backer M, Vanthienen J, Baesens B (2012) A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf Syst 37:654–676. doi:10.1016/j.is.2012.02.004

    Article  Google Scholar 

  • De Weerdt J, Vanthienen J, Baesens B (2013) Active trace clustering for improved process discovery. IEEE T Knowl Data Eng 25:2708–2720. doi:10.1109/TKDE.2013.64

    Article  Google Scholar 

  • Ferreira DR, Alves C (2012) Discovering user communities in large event logs. Lect Notes Bus Inf 99:123–134. doi:10.1007/978-3-642-28108-2_11

    Article  Google Scholar 

  • Ferreira DR, Szimanski F, Ralha CG (2013) Mining the low-level behavior of agents in high-level business processes. Int J Bus Integr Manag 6:146–166. doi:10.1504/ijbpim.2013.054678

    Google Scholar 

  • Greco G, Guzzo A, Ponieri L, Sacca D (2006) Discovering expressive process models by clustering log traces. IEEE T Knowl Data Eng 18:1010–1027. doi:10.1109/TKDE.2006.123

    Article  Google Scholar 

  • Günther CW, Rozinat A, van der Aalst WMP (2010) Activity mining by global trace segmentation. Lect Notes Bus Inf 43:128–139. doi:10.1007/978-3-642-12186-9_13

    Article  Google Scholar 

  • Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182. doi:10.1162/153244303322753616

    Google Scholar 

  • Huang Z, Lu X, Duan H (2011) Mining association rules to support resource allocation in business process management. Expert Syst Appl 38:9483–9490. doi:10.1016/j.eswa.2011.01.146

    Article  Google Scholar 

  • Johnson BT, Eagly AH (2000) Quantitative synthesis of social psychological research. In: Reis T, Judd CM (eds) Handbook of research methods in social and personality psychology. Cambridge University Press, Cambridge

    Google Scholar 

  • Kelton WD, Sadowski P, Swets NB (2010) Simulation with Arena. McGraw-Hill, New York

    Google Scholar 

  • Law AM (2007) Simulation modeling and analysis. McGraw-Hill, New York

    Google Scholar 

  • Leemans SJ, Fahland D, van der Aalst WMP (2013) Discovering block-structured process models from event logs - a constructive approach. In: Colom J-M, Desel J (eds) Application and theory of Petri nets and concurrency. Lecturer notes in computer science, vol 7927. Springer, Berlin, Heidelberg, pp 311–329. doi:10.1007/978-3-642-38697-8_17

    Chapter  Google Scholar 

  • Liu Y, Wang J, Yang Y, Sun J (2008) A semi-automatic approach for workflow staff assignment. Comput Ind 59:463–476. doi:10.1016/j.compind.2007.12.002

    Article  Google Scholar 

  • Liu Y, Zhang H, Li C, Jiao RJ (2012) Workflow simulation for operational decision support using event graph through process mining. Decis Support Syst 52:685–697. doi:10.1016/j.dss.2011.11.003

    Article  Google Scholar 

  • Ly TL, Rinderle S, Dadam P, Reichert M (2006) Mining staff assignment rules from event-based data. Lect Notes Comput Sci 3812:177–190. doi:10.1007/11678564_16

    Article  Google Scholar 

  • Martin N, Depaire B, Caris A (2014a) Event log knowledge as a complementary simulation model construction input. In: Proceedings of the 4th international conference on simulation and modeling methodologies, technologies and applications, pp 456–462. doi:10.5220/0005100404560462

  • Martin N, Depaire B, Caris A (2014b) The use of process mining in a business process simulation context: overview and challenges. In: Proceedings of the 2014 IEEE symposium on computational intelligence and data mining, pp 381–388. doi:10.1109/CIDM.2014.7008693

  • Martin N, Depaire B, Caris A (2015a) Using event logs to model interarrival times in business process simulation. Lect Notes Bus Inf (forthcoming)

  • Martin N, Depaire B, Caris A (2015b) Using process mining to model interarrival times: investigating the sensitivity of the ARPRA framework. In: Proceedings of the 2015 winter simulation conference (forthcoming)

  • Măruşter L, van Beest NRTP (2009) Redesigning business processes: a methodology based on simulation and process mining techniques. Knowl Inf Syst 21:267–297. doi:10.1007/s10115-009-0224-0

    Article  Google Scholar 

  • Melão N, Pidd M (2003) Use of business process simulation: a survey of practitioners. J Oper Res Soc 54:2–10. doi:10.1057/palgrave.jors.2601477

    Article  Google Scholar 

  • Nakatumba J (2013) Resource-aware business process management: analysis and support. Dissertation, Eindhoven University of Technology

  • Nakatumba J, van der Aalst WMP (2010) Analyzing resource behavior using process mining. Lect Notes Bus Inf 43:69–80. doi:10.1007/978-3-642-12186-9_8

    Article  Google Scholar 

  • Nakatumba J, Westergaard M, van der Aalst WMP (2012) Generating event logs with workload-dependent speeds from simulation models. Lect Notes Bus Inf 112:383–397. doi:10.1007/978-3-642-31069-0_31

    Article  Google Scholar 

  • Pika A, van der Aalst WMP, Fidge CJ, ter Hofstede AHM, Wynn MT (2013) Predicting deadline transgressions using event logs. Lect Notes Bus Inf 132:211–216. doi:10.1007/978-3-642-36285-9_22

    Article  Google Scholar 

  • Pospíšil M, Hruška T (2012) Business process simulation for predictions. In: BUSTECH 2012, the second international conference on business intelligence and technology, pp 14–18

  • Pospíšil M, Mates V, Hruška T, Bartik V (2013) Process mining in a manufacturing company for predictions and planning. Int J Adv Softw 6:293–297

    Google Scholar 

  • Robinson S (2004) Simulation. Wiley, Chichester

    Google Scholar 

  • Rogge-Solti A, Kasneci G (2014) Temporal anomaly detection in business processes. Lect Notes Comput Sci 8659:234–249. doi:10.1007/978-3-319-10172-9_15

    Article  Google Scholar 

  • Rogge-Solti A, van der Aalst WMP, Weske M (2014) Discovering stochastic Petri nets with arbitrary delay distributions from event logs. Lect Notes Bus Inf 171:15–27. doi:10.1007/978-3-319-06257-0_2

    Article  Google Scholar 

  • Rozinat A, van der Aalst WMP (2006a) Decision mining in ProM. Lect Notes Comput Sci 4102:420–425. doi:10.1007/11841760_33

    Article  Google Scholar 

  • Rozinat A, van der Aalst WMP (2006b) Decision mining in business processes. BPM Center Report BPM-06-10

  • Rozinat A, Mans RS, Song M, van der Aalst WMP (2008a) Discovering colored Petri nets from event logs. Int J Softw Tools Technol Transf 10:57–74. doi:10.1007/s10009-007-0051-0

    Article  Google Scholar 

  • Rozinat A, Wynn MT, van der Aalst WMP, ter Hofstede A, Fidge CJ (2008b) Workflow simulation for operational decision support using design, historic and state information. Lect Notes Comput Sci 5240:196–211. doi:10.1007/978-3-540-85758-7_16

    Article  Google Scholar 

  • Rozinat A, Mans RS, Song M, van der Aalst WMP (2009) Discovering simulation models. Inf Syst 34:305–327. doi:10.1016/j.is.2008.09.002

    Article  Google Scholar 

  • Senderovich A, Weidlich M, Gal A, Mandelbaum A (2014a) Mining resource-scheduling protocols. Lect Notes Comput Sci 8659:200–216. doi:10.1007/978-3-319-10172-9_13

    Article  Google Scholar 

  • Senderovich A, Weidlich M, Gal A, Mandelbaum A (2014b) Queue mining – predicting delays in service processes. In: Jarke M, Mylopoulos J, Quix C, Rolland C, Manolopoulos Y, Mouratidis H, Horkoff J (eds) Advanced information systems engineering. Lecturer notes in computer science, vol 8484. Springer, Berlin, Heidelberg, pp 42–57. doi:10.1007/978-3-319-07881-6_4

    Google Scholar 

  • Senderovich A, Weidlich M, Gal A, Mandelbaum A (2015a) Queue mining for delay prediction in multi-class service processes. Inf Syst 53:278–295. doi:10.1016/j.is.2015.03.010

    Article  Google Scholar 

  • Senderovich A, Leemans S, Harel S, Gal A, Mandelbaum A, van der Aalst W (2015b) Discovering queues from event logs with varying levels of information. Lect Notes Bus Inf (forthcoming)

  • Song M, van der Aalst WMP (2007) Supporting process mining by showing events at a glance. In: Proceedings of 17th annual workshop on information technologies and systems, pp 139–145

  • Song M, van der Aalst WMP (2008) Towards comprehensive support for organizational mining. Decis Support Syst 46:300–317. doi:10.1016/j.dss.2008.07.002

    Article  Google Scholar 

  • Song M, Günther CW, van der Aalst WMP (2009) Trace clustering in process mining. Lect Notes Bus Inf 17:109–120. doi:10.1007/978-3-642-00328-8_11

    Article  Google Scholar 

  • Szimanski F, Ralha CG, Wagner G, Ferreira DR (2013) Improving business process models with agent-based simulation and process mining. Lect Notes Bus Inf 147:124–138. doi:10.1007/978-3-642-38484-4_10

    Article  Google Scholar 

  • Tumay K (1996) Business process simulation. In: Proceedings of the 1996 winter simulation conference, pp 55–60. doi:10.1109/WSC.1995.478705

  • Van Beest NRTP, Măruşter L (2007) A process mining approach to redesign business processes – a case study in gas industry. In: Ninth international symposium on symbolic and numeric algorithms for scientific computing, pp 541–548. doi:10.1109/SYNASC.2007.50

  • van der Aalst WMP (1998) The application of Petri nets to workflow management. J Circuit Syst Comput 8:21–66. doi:10.1142/S0218126698000043

    Article  Google Scholar 

  • van der Aalst WMP (2011) Process mining: discovery, conformance and enhancement of business processes. Springer, Heidelberg

    Book  Google Scholar 

  • van der Aalst WMP (2013a) Business process management: a comprehensive survey. ISRN Softw Eng 2013:37. doi:10.1155/2013/507984

    Google Scholar 

  • van der Aalst WMP (2013b) Business process simulation survival guide. BPM Center Report BPM-13-11

  • van der Aalst WMP (2013c) Process cubes: slicing, dicing, rolling up and drilling down event data for process mining. Lect Notes Bus Inf 159:1–22. doi:10.1007/978-3-319-02922-1_1

    Article  Google Scholar 

  • van der Aalst WMP (2015) Extracting event data from databases to unleash process mining. In: vom Brocke J, Schmiedel T (eds) BPM—driving innovation in a digital world. Springer, Heidelberg

    Google Scholar 

  • van der Aalst WMP, Nakatumba J, Rozinat A, Russel N (2010) Business process simulation. In: vom Brocke J, Rosemann M (eds) Handbook of business process management. Springer, Heidelberg

    Google Scholar 

  • van der Aalst WMP, Schonenberg MH, Song M (2011) Time prediction based on process mining. Inf Syst 36:450–475. doi:10.1016/j.is.2010.09.001

    Article  Google Scholar 

  • van der Aalst W, Adriansyah A, Alves de Medeiros AK, Arcieri F, Baier T, Blickle T, Bose JC, van den Brand P, Brandtjen R, Buijs J, Burattin A, Carmona J, Castellanos M, Claes J, Cook J, Costantini N, Curbera F, Damiani E, de Leoni M, Delias P, van Dongen BF, Dumas M, Dustdar S, Fahland D, Ferreira DR, Gaaloul W, van Geffen F, Goel S, Günther C, Guzzo A, Harmon P, ter Hofstede A, Hoogland J, Ingvaldsen JE, Kato K, Kuhn R, Kumar A, La Rosa M, Maggi F, Malerba D, Mans RS, Manuel A, McCreesh M, Mello P, Mendling J, Montali M, Motahari-Nezhad HR, zur Muehlen M, Muñoz-Gama J, Pontieri L, Ribeiro J, Rozinat A, Seguel Pérez H, Seguel Pérez R, Sepúlveda M, Sinur J, Soffer P, Song M, Sperduti A, Stilo G, Stoel C, Swenson K, Talamo M, Tan W, Turner C, Vanthienen J, Varvaressos G, Verbeek E, Verdonk M, Vigo R, Wang J, Weber B, Weidlich M, Weijters T, Wen L, Westergaard M, Wynn M (2012a) Process mining manifesto. Lect Notes Bus Inf 99:169–194. doi:10.1007/978-3-642-28108-2_19

    Article  Google Scholar 

  • van der Aalst WMP, Adriansyah A, van Dongen B (2012b) Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip Rev Data Min Knowl Discov 2:182–192. doi:10.1002/widm.1045

    Article  Google Scholar 

  • Veiga GM, Ferreira DR (2010) Understanding spaghetti models with sequence clustering in ProM. Lect Notes Bus Inf 43:92–103. doi:10.1007/978-3-642-12186-9_10

    Article  Google Scholar 

  • Verbeek HMW, Buijs JCAM, van Dongen BF, van der Aalst WMP (2010) ProM 6: the process mining toolkit. CEUR Workshop Proc 615:34–39

    Google Scholar 

  • Wombacher A, Iacob ME (2013) Start time and duration estimation in semi-structured processes. In: Proceedings of the 28th annual ACM symposium on applied computing, pp 1403–1409. doi:10.1145/2480362.2480626

  • Wombacher A, Iacob M, Haitsma M (2011) Towards a performance estimate in semi-structured processes. In: Proceedings of the 2011 IEEE international conference on service-oriented computing and applications, pp 1–5. doi:10.1109/soca.2011.6166256

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Niels Martin.

Additional information

Accepted after two revisions by the editors of the special issue.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Martin, N., Depaire, B. & Caris, A. The Use of Process Mining in Business Process Simulation Model Construction. Bus Inf Syst Eng 58, 73–87 (2016). https://doi.org/10.1007/s12599-015-0410-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12599-015-0410-4

Keywords

Navigation