Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Data-Driven Process Simulation

  • Benoît DepaireEmail author
  • Niels MartinEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_102


Data-driven process simulation is a technique which constructs a computer model that imitates the internal details of a business process and extensively uses data – recorded by information systems supporting the actual process – to do so. The model is used to execute what-if scenarios in order to better understand the actual process behavior and predict the impact of potential changes to the process.


Data-Driven Process Simulation

Every organization executes multiple business processes – e.g., the production, transportation, and billing process – which have to be managed properly to generate customer value (Dumas et al. 2013). An essential part of business process management is the identification and design of process improvement opportunities – e.g., hire more staff to reduce waiting time at a specific step in the process. Since a business process typically has a complex and dynamic nature, it is often impossible to deduce analytically the full impact of a...

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  1. Aguirre S, Parra C, Alvarado J (2013) Combination of process mining and simulation techniques for business process redesign: a methodological approach. Lect Notes Bus Info Process 162:24–43CrossRefGoogle Scholar
  2. Baier T, Mendling J, Weske M (2014) Bridging abstraction layers in process mining. Info Syst 46:123–139CrossRefGoogle Scholar
  3. 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 401–412Google Scholar
  4. Bose RPJC, van der Aalst WMP (2010) Trace clustering based on conserved patterns: towards achieving better process models. Lect Notes Bus Info Process 43:170–181CrossRefGoogle Scholar
  5. 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 103–110Google Scholar
  6. de Leoni M, Dumas M, García-Bañuelos L (2013) Discovering branching conditions from business process execution logs. Lect Notes Comput Sci 7793: 114–129CrossRefGoogle Scholar
  7. de Medeiros AKA, Guzzo A, Greco G, van der Aalst WMP, Saccà D (2008) Process mining based on clustering: a quest for precision. Lect Notes Comput Sci 4928:17–29CrossRefGoogle Scholar
  8. 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. Info Syst 37(7):654–676CrossRefGoogle Scholar
  9. De Weerdt J, Vanthienen J, Baesens B, vanden Broucke SKLM (2013) Active trace clustering for improved process discovery. IEEE Trans Knowl Data Eng 25(12):2708–2720Google Scholar
  10. Delias P, Doumpos M, Grigoroudis E, Manolitzas P, Matsatsinis N (2015) Supporting healthcare management decisions via robust clustering of event logs. Knowl Based Syst 84:203–213CrossRefGoogle Scholar
  11. Dickey D, Pearson C (2005) Recency effect in college student course evaluations. Pract Assess Res Eval 10(6):1–10Google Scholar
  12. Dumas M, van der Aalst WMP, Ter Hofstede AH (2005) Process-aware information systems: bridging people and software through process technology. Wiley, HobokenCrossRefGoogle Scholar
  13. Dumas M, La Rosa M, Mendling J, Reijers HA (2013) Fundamentals of business process management. Springer, HeidelbergCrossRefGoogle Scholar
  14. Ferreira DR, Alves C (2012) Discovering user communities in large event logs. Lect Notes Bus Info Process 99:123–134CrossRefGoogle Scholar
  15. Greco G, Guzzo A, Ponieri L, Sacca D (2006) Discovering expressive process models by clustering log traces. IEEE Trans Knowl Data Eng 18(8):1010–1027CrossRefGoogle Scholar
  16. Günther CW, Rozinat A, van der Aalst WMP (2010) Activity mining by global trace segmentation. Lect Notes Bus Info Process 43:128–139CrossRefGoogle Scholar
  17. Hopp WJ, Spearman ML (2011) Factory physics. Waveland Press, Long GroveGoogle Scholar
  18. Huang Z, Lu X, Duan H (2011) Mining association rules to support resource allocation in business process management. Expert Syst Appl 38(8):9483–9490CrossRefGoogle Scholar
  19. Kelton W, Sadowski R, Zupick N (2015) Simulation with Arena. McGraw-Hill, New YorkGoogle Scholar
  20. Leyer M, Moormann J (2015) Comparing concepts for shop floor control of information-processing services in a job shop setting: a case from the financial services sector. Int J Prod Res 53(4):1168–1179CrossRefGoogle Scholar
  21. Liu J, Hu J (2007) Dynamic batch processing in workflows: model and implementation. Futur Gener Comput Syst 23(3):338–347MathSciNetCrossRefGoogle Scholar
  22. Liu Y, Wang J, Yang Y, Sun J (2008) A semi-automatic approach for workflow staff assignment. Comput Ind 59(5):463–476CrossRefGoogle Scholar
  23. 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(3):685–697CrossRefGoogle Scholar
  24. Ly LT, Rinderle S, Dadam P, Reichert M (2006) Mining staff assignment rules from event-based data. Lect Notes Comput Sci 3812:177–190CrossRefGoogle Scholar
  25. Mannhardt F, de Leoni M, Reijers HA, van der Aalst WMP, Toussaint J (2016) From low-level events to activities – a pattern-based approach. Lect Notes Comput Sci 9850:125–141CrossRefGoogle Scholar
  26. Martin N, Bax F, Depaire B, Caris A (2016a) Retrieving resource availability insights from event logs. In: Proceedings of the 2016 IEEE international conference on enterprise distributed object computing, pp 69–78Google Scholar
  27. Martin N, Depaire B, Caris A (2016b) The use of process mining in business process simulation model construction: structuring the field. Bus Info Syst Eng 58(1): 73–87CrossRefGoogle Scholar
  28. Martin N, Depaire B, Caris A (2016c) Using event logs to model interarrival times in business process simulation. Lect Notes Bus Info Process 256:255–267CrossRefGoogle Scholar
  29. Martin N, Swennen M, Depaire B, Jans M, Caris A, Vanhoof K (2017) Retrieving batch organisation of work insights from event logs. Decis Support Syst 100:119–128CrossRefGoogle Scholar
  30. Melão N, Pidd M (2003) Use of business process simulation: a survey of practitioners. J Oper Res Soc 54(1): 2–10zbMATHCrossRefGoogle Scholar
  31. Măruşter L, van Beest NRTP (2009) Redesigning business processes: a methodology based on simulation and process mining techniques. Knowl Inf Syst 21(3):267–297CrossRefGoogle Scholar
  32. Nakatumba J (2013) Resource-aware business process management: analysis and support. Ph.D. thesis, Eindhoven University of TechnologyGoogle Scholar
  33. Nakatumba J, van der Aalst WMP (2010) Analyzing resource behavior using process mining. Lect Notes Bus Info Process 43:69–80CrossRefGoogle Scholar
  34. Nakatumba J, Westergaard M, van der Aalst WMP (2012) Generating event logs with workload-dependent speeds from simulation models. Lect Notes Bus Info Process 112:383–397Google Scholar
  35. Pika A, van der Aalst WMP, Fidge CJ, ter Hofstede AHM, Wynn MT (2013) Predicting deadline transgressions using event logs. Lect Notes Bus Info Process 132: 211–216CrossRefGoogle Scholar
  36. Pospisil M, Hrus̆ka T (2012) Business process simulation for predictions. In: Proceedings of the second international conference on business intelligence and technology, pp 14–18Google Scholar
  37. Robinson S (2004) Simulation: the practice of model development and use. Wiley, ChichesterGoogle Scholar
  38. Rogge-Solti A, Kasneci G (2014) Temporal anomaly detection in business processes. Lect Notes Comput Sci 8659:234–249CrossRefGoogle Scholar
  39. Rozinat A, van der Aalst WMP (2006a) Decision mining in business processes. Tech. Rep. BPM Center Report BPM-06-10Google Scholar
  40. Rozinat A, van der Aalst WMP (2006b) Decision mining in ProM. Lect Notes Comput Sci 4102:420–425CrossRefGoogle Scholar
  41. Rozinat A, Mans RS, Song M, van der Aalst WMP (2008) Discovering colored Petri nets from event logs. Int J Softw Tools Technol Transfer 10(1):57–74CrossRefGoogle Scholar
  42. Rozinat A, Mans RS, Song M, van der Aalst WMP (2009) Discovering simulation models. Info Syst 34(3): 305–327CrossRefGoogle Scholar
  43. Schonenberg H, Jian J, Sidorova N, van der Aalst WMP (2010) Business trend analysis by simulation. Lect Notes Comput Sci 6051:515–529Google Scholar
  44. Senderovich A, Weidlich M, Gal A, Mandelbaum A (2014) Mining resource scheduling protocols. Lect Notes Comput Sci 8659:200–216CrossRefGoogle Scholar
  45. Song M, van der Aalst WMP (2008) Towards comprehensive support for organizational mining. Decis Support Syst 46(1):300–317CrossRefGoogle Scholar
  46. Song M, Günther CW, van der Aalst WMP (2009) Trace clustering in process mining. Lect Notes Bus Info Process 17:109–120CrossRefGoogle Scholar
  47. Suriadi S, Wynn MT, Xu J, van der Aalst WMP, ter Hofstede AH (2017) Discovering work prioritisation patterns from event logs. Decis Support Syst 100: 77–92CrossRefGoogle Scholar
  48. Szimanski F, Ralha CG, Wagner G, Ferreira DR (2013) Improving business process models with agent-based simulation and process mining. Lect Notes Bus Info Process 147:124–138CrossRefGoogle Scholar
  49. Tumay K (1996) Business process simulation. In: Proceedings of the 1996 winter simulation conference, pp 55–60Google Scholar
  50. van Beest NRTP, Măruşter L (2007) A process mining approach to redesign business processes – a case study in gas industry. In: Proceedings of the 2007 international symposium on symbolic and numeric algorithms for scientific computing, pp 541–548Google Scholar
  51. van der Aalst WMP (2015) Business process simulation survival guide. In: vom Brocke J, Rosemann M (eds) Handbook on business process management, vol 1. Springer, Heidelberg, pp 337–370Google Scholar
  52. van der Aalst WMP (2016) Process mining: data science in action. Springer, HeidelbergCrossRefGoogle Scholar
  53. van der Aalst WMP, Nakatumba J, Rozinat A, Russell N (2010) Business process simulation. In: vom Brocke J, Rosemann M (eds) Handbook on business process management. Springer, Heidelberg, pp 313–338Google Scholar
  54. Veiga GM, Ferreira DR (2010) Understanding spaghetti models with sequence clustering in ProM. Lect Notes Bus Info Process 43:92–103CrossRefGoogle Scholar
  55. Vincent S (1998) Input data analysis. In: Banks J (ed) Handbook of simulation: principles, advances, applications, and practice. Wiley, Hoboken, pp 3–30Google Scholar
  56. Wen Y, Chen Z, Liu J, Chen J (2013) Mining batch processing workflow models from event logs. Concurrency Comput Pract Experience 25(13):1928–1942.CrossRefGoogle Scholar
  57. Wombacher A, Iacob ME (2013) Start time and duration distribution estimation in semi-structured processes. In: Proceedings of the 28th annual ACM symposium on applied computing, pp 1403–1409Google Scholar
  58. 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 1–5Google Scholar
  59. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw16(3):645–678CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Research group Business InformaticsHasselt UniversityDiepenbeekBelgium