Definitions
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 ā recorded by information systems supporting the actual process ā 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.
Overview
Data-Driven Process Simulation
Every organization executes multiple business processes ā e.g., the production, transportation, and billing process ā 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 ā 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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References
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 162:24ā43
Baier T, Mendling J, Weske M (2014) Bridging abstraction layers in process mining. Info Syst 46:123ā139
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 401ā412
Bose RPJC, van der Aalst WMP (2010) Trace clustering based on conserved patterns: towards achieving better process models. Lect Notes Bus Info Process 43:170ā181
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 103ā110
de Leoni M, Dumas M, GarcĆa-BaƱuelos L (2013) Discovering branching conditions from business process execution logs. Lect Notes Comput Sci 7793: 114ā129
de Medeiros AKA, Guzzo A, Greco G, van der Aalst WMP, SaccĆ D (2008) Process mining based on clustering: a quest for precision. Lect Notes Comput Sci 4928:17ā29
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):654ā676
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):2708ā2720
Delias P, Doumpos M, Grigoroudis E, Manolitzas P, Matsatsinis N (2015) Supporting healthcare management decisions via robust clustering of event logs. Knowl Based Syst 84:203ā213
Dickey D, Pearson C (2005) Recency effect in college student course evaluations. Pract Assess Res Eval 10(6):1ā10
Dumas M, van der Aalst WMP, Ter Hofstede AH (2005) Process-aware information systems: bridging people and software through process technology. Wiley, Hoboken
Dumas M, La Rosa M, Mendling J, Reijers HA (2013) Fundamentals of business process management. Springer, Heidelberg
Ferreira DR, Alves C (2012) Discovering user communities in large event logs. Lect Notes Bus Info Process 99:123ā134
Greco G, Guzzo A, Ponieri L, Sacca D (2006) Discovering expressive process models by clustering log traces. IEEE Trans Knowl Data Eng 18(8):1010ā1027
GĆ¼nther CW, Rozinat A, van der Aalst WMP (2010) Activity mining by global trace segmentation. Lect Notes Bus Info Process 43:128ā139
Hopp WJ, Spearman ML (2011) Factory physics. Waveland Press, Long Grove
Huang Z, Lu X, Duan H (2011) Mining association rules to support resource allocation in business process management. Expert Syst Appl 38(8):9483ā9490
Kelton W, Sadowski R, Zupick N (2015) Simulation with Arena. McGraw-Hill, New York
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):1168ā1179
Liu J, Hu J (2007) Dynamic batch processing in workflows: model and implementation. Futur Gener Comput Syst 23(3):338ā347
Liu Y, Wang J, Yang Y, Sun J (2008) A semi-automatic approach for workflow staff assignment. Comput Ind 59(5):463ā476
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):685ā697
Ly LT, Rinderle S, Dadam P, Reichert M (2006) Mining staff assignment rules from event-based data. Lect Notes Comput Sci 3812:177ā190
Mannhardt F, de Leoni M, Reijers HA, van der Aalst WMP, Toussaint J (2016) From low-level events to activities ā a pattern-based approach. Lect Notes Comput Sci 9850:125ā141
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 69ā78
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): 73ā87
Martin N, Depaire B, Caris A (2016c) Using event logs to model interarrival times in business process simulation. Lect Notes Bus Info Process 256:255ā267
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 100:119ā128
MelĆ£o N, Pidd M (2003) Use of business process simulation: a survey of practitioners. J Oper Res Soc 54(1): 2ā10
MÄruÅter L, van Beest NRTP (2009) Redesigning business processes: a methodology based on simulation and process mining techniques. Knowl Inf Syst 21(3):267ā297
Nakatumba J (2013) Resource-aware business process management: analysis and support. Ph.D. thesis, Eindhoven University of Technology
Nakatumba J, van der Aalst WMP (2010) Analyzing resource behavior using process mining. Lect Notes Bus Info Process 43:69ā80
Nakatumba J, Westergaard M, van der Aalst WMP (2012) Generating event logs with workload-dependent speeds from simulation models. Lect Notes Bus Info Process 112:383ā397
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: 211ā216
Pospisil M, HrusĢka T (2012) Business process simulation for predictions. In: Proceedings of the second international conference on business intelligence and technology, pp 14ā18
Robinson S (2004) Simulation: the practice of model development and use. Wiley, Chichester
Rogge-Solti A, Kasneci G (2014) Temporal anomaly detection in business processes. Lect Notes Comput Sci 8659:234ā249
Rozinat A, van der Aalst WMP (2006a) Decision mining in business processes. Tech. Rep. BPM Center Report BPM-06-10
Rozinat A, van der Aalst WMP (2006b) Decision mining in ProM. Lect Notes Comput Sci 4102:420ā425
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):57ā74
Rozinat A, Mans RS, Song M, van der Aalst WMP (2009) Discovering simulation models. Info Syst 34(3): 305ā327
Schonenberg H, Jian J, Sidorova N, van der Aalst WMP (2010) Business trend analysis by simulation. Lect Notes Comput Sci 6051:515ā529
Senderovich A, Weidlich M, Gal A, Mandelbaum A (2014) Mining resource scheduling protocols. Lect Notes Comput Sci 8659:200ā216
Song M, van der Aalst WMP (2008) Towards comprehensive support for organizational mining. Decis Support Syst 46(1):300ā317
Song M, GĆ¼nther CW, van der Aalst WMP (2009) Trace clustering in process mining. Lect Notes Bus Info Process 17:109ā120
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: 77ā92
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 147:124ā138
Tumay K (1996) Business process simulation. In: Proceedings of the 1996 winter simulation conference, pp 55ā60
van Beest NRTP, MÄruÅter L (2007) A process mining approach to redesign business processes ā a case study in gas industry. In: Proceedings of the 2007 international symposium on symbolic and numeric algorithms for scientific computing, pp 541ā548
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 337ā370
van der Aalst WMP (2016) Process mining: data science in action. Springer, Heidelberg
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 313ā338
Veiga GM, Ferreira DR (2010) Understanding spaghetti models with sequence clustering in ProM. Lect Notes Bus Info Process 43:92ā103
Vincent S (1998) Input data analysis. In: Banks J (ed) Handbook of simulation: principles, advances, applications, and practice. Wiley, Hoboken, pp 3ā30
Wen Y, Chen Z, Liu J, Chen J (2013) Mining batch processing workflow models from event logs. Concurrency Comput Pract Experience 25(13):1928ā1942.
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 1403ā1409
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 1ā5
Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw16(3):645ā678
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Depaire, B., Martin, N. (2019). Data-Driven Process Simulation. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_102
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