Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Queue Mining

  • Arik Senderovich
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_101-1


Queue mining is a set of data-driven methods (models and algorithms) for queueing analysis of business processes. Prior to queue mining, process mining techniques overlooked dependencies between cases when answering such operational questions. To address this gap, queue mining draws from analytical approaches from queueing theory and combines them with classical process mining techniques.


Modern business processes are supported by information systems that record process-related events in event logs. Process mining is a maturing research field that aims at discovering useful information about the business process from these event logs (van der Aalst 2011). Process mining can be viewed as the link that connects process analysis fields (e.g., business process management and operations research) to data analysis fields (e.g., machine learning and data mining) (van der Aalst 2012).

This entry is focused on process mining techniques that aim at answering operational- or...

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  1. Bolch G, Greiner S, de Meer H, Trivedi KS (2006) Queueing networks and Markov chains – modeling and performance evaluation with computer science applications, 2nd edn. Wiley, HobokenCrossRefGoogle Scholar
  2. Bramson M (2008) Stability of queueing networks. Springer, Berlin/HeidelbergMathSciNetCrossRefGoogle Scholar
  3. Burattin A, Sperduti A, Veluscek M (2013) Business models enhancement through discovery of roles. In: 2013 IEEE symposium on computational intelligence and data mining (CIDM). IEEE, pp 103–110Google Scholar
  4. Chen H, Yao DD (2013) Fundamentals of queueing networks: performance, asymptotics, and optimization, vol 46. Springer Science & Business Media, New YorkGoogle Scholar
  5. 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 56:235–257Google Scholar
  6. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232.  https://doi.org/10.1214/aos/1013203451
  7. Gal A, Mandelbaum A, Schnitzler F, Senderovich A, Weidlich M (2015) Traveling time prediction in scheduled transportation with journey segments. Inf Syst 64:266–280CrossRefGoogle Scholar
  8. Glynn PW, Iglehart DL (1988) Simulation methods for queues: an overview. Queueing Syst 3(3):221–255MathSciNetCrossRefGoogle Scholar
  9. Haas PJ (2002) Stochastic petri nets: modelling, stability, simulation. Springer, New YorkGoogle Scholar
  10. Hall RW (1991) Queueing methods: for services and manufacturing. Prentice Hall, Englewood CliffsGoogle Scholar
  11. Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer series in statistics. Springer, New YorkCrossRefGoogle Scholar
  12. Ibrahim R, Whitt W (2009) Real-time delay estimation based on delay history. Manuf Serv Oper Manag 11(3):397–415CrossRefGoogle Scholar
  13. Jackson JR (1957) Networks of waiting lines. Oper Res 5(4):518–521MathSciNetCrossRefGoogle Scholar
  14. Kelly FP (1975) Networks of queues with customers of different types. J Appl Probab 12(3):542–554MathSciNetCrossRefGoogle Scholar
  15. Leontjeva A, Conforti R, Francescomarino CD, Dumas M, Maggi FM (2015) Complex symbolic sequence encodings for predictive monitoring of business processes. In: Proceedings of business process management – 13th international conference, BPM 2015, Innsbruck, 31 Aug–3 Sep 2015, pp 297–313Google Scholar
  16. Liu T, Cheng Y, Ni Z (2012) Mining event logs to support workflow resource allocation. Knowl-Based Syst 35:320–331CrossRefGoogle Scholar
  17. 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: 119–128. https://doi.org/10.1016/j.dss.2017.02.012CrossRefGoogle Scholar
  18. Măruşter L, van Beest NR (2009) Redesigning business processes: a methodology based on simulation and process mining techniques. Knowl Inf Syst 21(3): 267–297CrossRefGoogle Scholar
  19. Nakatumba J, van der Aalst WMP (2009) Analyzing resource behavior using process mining. In: Rinderle-Ma S, Sadiq SW, Leymann F (eds) Business process management workshops, BPM 2009 international workshops, Ulm, 7 Sept 2009, Revised Papers. Lecture notes in business information processing, vol 43. Springer, pp 69–80. https://doi.org/10.1007/978-3-642-12186-9_8CrossRefGoogle Scholar
  20. Nakatumba J, Westergaard M, van der Aalst WMP (2012) Generating event logs with workload-dependent speeds from simulation models. In: Proceedings of advanced information systems engineering workshops – CAiSE 2012 international workshops, Gdańsk, 25–26 June 2012, pp 383–397. https://doi.org/10.1007/978-3-642-31069-0_31Google Scholar
  21. Pinedo M (2012) Scheduling. Springer, New YorkCrossRefGoogle Scholar
  22. Polato M, Sperduti A, Burattin A, de Leoni M (2016) Time and activity sequence prediction of business process instances. CoRR abs/1602.07566Google Scholar
  23. Rogge-Solti A, Weske M (2015) Prediction of business process durations using non-markovian stochastic petri nets. Inf Syst 54:1–14CrossRefGoogle Scholar
  24. Rozinat A, Mans R, Song M, van der Aalst W (2009) Discovering simulation models. Inf Syst 34(3): 305–327CrossRefGoogle Scholar
  25. Rubinstein RY (1986) Monte Carlo optimization, simulation, and sensitivity of queueing networks. Wiley, New YorkGoogle Scholar
  26. Senderovich A, Weidlich M, Gal A, Mandelbaum A (2014) Mining resource scheduling protocols. In: Business process management. Springer, pp 200–216Google Scholar
  27. Senderovich A, Rogge-Solti A, Gal A, Mendling J, Mandelbaum A, Kadish S, Bunnell CA (2015a) Data-driven performance analysis of scheduled processes. In: Business process management. Springer, pp 35–52Google Scholar
  28. Senderovich A, Weidlich M, Gal A, Mandelbaum A (2015b) Queue mining for delay prediction in multi-class service processes. Inf Syst 53:278–295CrossRefGoogle Scholar
  29. Senderovich A, Weidlich M, Gal A, Mandelbaum A, Kadish S, Bunnell CA (2015c) Discovery and validation of queueing networks in scheduled processes. In: Advanced information systems engineering. Springer, pp 417–433CrossRefGoogle Scholar
  30. Senderovich A, Shleyfman A, Weidlich M, Gal A, Mandelbaum A (2016a) P ˆ3-folder: optimal model simplification for improving accuracy in process performance prediction. In: Proceedings of business process management – 14th international conference, BPM 2016, Rio de Janeiro, 18–22 Sept 2016, pp 418–436Google Scholar
  31. Senderovich A, Weidlich M, Yedidsion L, Gal A, Mandelbaum A, Kadish S, Bunnell CA (2016b) Conformance checking and performance improvement in scheduled processes: a queueing-network perspective. Inf Syst 62:185–206CrossRefGoogle Scholar
  32. Song M, Van der Aalst WM (2008) Towards comprehensive support for organizational mining. Decis Support Syst 46(1):300–317CrossRefGoogle Scholar
  33. van der Aalst WMP (2011) Process mining – discovery, conformance and enhancement of business processes. Springer, Berlin/HeidelbergzbMATHGoogle Scholar
  34. van der Aalst WMP (2012) Process mining: overview and opportunities. ACM Trans Manag Inf Syst 3(2):7Google Scholar
  35. van der Aalst WMP, Schonenberg MH, Song M (2011) Time prediction based on process mining. Inf Syst 36(2):450–475CrossRefGoogle Scholar
  36. Whitt W (1983) The queueing network analyzer. Bell Syst Techn J 62(9):2779–2815CrossRefGoogle Scholar
  37. Whitt W (1999) Predicting queueing delays. Manag Sci 45(6):870–888CrossRefGoogle Scholar
  38. Wickens CD, Hollands JG, Banbury S, Parasuraman R (2015) Engineering psychology & human performance. Psychology Press, New YorkGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Mechanical and Industrial EngineeringUniversity of TorontoTorontoCanada

Section editors and affiliations

  • Marlon Dumas
    • 1
  • Matthias Weidlich
    • 2
  1. 1.Institute of Computer ScienceUniversity of TartuTartuEstonia
  2. 2.Department of Computer ScienceHumboldt-Universität zu BerlinBerlinGermany