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...
- 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
- Chen H, Yao DD (2013) Fundamentals of queueing networks: performance, asymptotics, and optimization, vol 46. Springer Science & Business Media, New YorkGoogle Scholar
- 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
- Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232. https://doi.org/10.1214/aos/1013203451
- Haas PJ (2002) Stochastic petri nets: modelling, stability, simulation. Springer, New YorkGoogle Scholar
- Hall RW (1991) Queueing methods: for services and manufacturing. Prentice Hall, Englewood CliffsGoogle Scholar
- 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
- 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
- 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
- Polato M, Sperduti A, Burattin A, de Leoni M (2016) Time and activity sequence prediction of business process instances. CoRR abs/1602.07566Google Scholar
- Rubinstein RY (1986) Monte Carlo optimization, simulation, and sensitivity of queueing networks. Wiley, New YorkGoogle Scholar
- Senderovich A, Weidlich M, Gal A, Mandelbaum A (2014) Mining resource scheduling protocols. In: Business process management. Springer, pp 200–216Google Scholar
- 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
- 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
- van der Aalst WMP (2012) Process mining: overview and opportunities. ACM Trans Manag Inf Syst 3(2):7Google Scholar
- Wickens CD, Hollands JG, Banbury S, Parasuraman R (2015) Engineering psychology & human performance. Psychology Press, New YorkGoogle Scholar