Synonyms
Definitions
In the context of business process analytics, decision discovery is the problem of discovering the set of rules that are de-facto used to make decisions in a business process, by analyzing event logs recording past executions of the process.
Overview
The execution of business processes typically follow some decisions that determine, within the multiple alternative executions, how to carry on executions of the process. These decisions are defined over the characteristics of the single instances (e.g. loan’s amount, applicant’s or patient’s age). This chapter reports on different approaches to discover these decision rules when unknown to process stakeholders. Most of these techniques in fact leverage on machine-learning. A number of successful real-life case-study applications are discussed along with the open-source tools that were used.
Introduction
The main focus of automatic process discovery has traditionally been in the realm of...
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References
Adriansyah A (2014) Aligning observed and modeled behavior. PhD thesis, Eindhoven University of Technology. https://doi.org/10.6100/IR770080
Batoulis K, Meyer A, Bazhenova E, Decker G, Weske M (2015) Extracting decision logic from process models. In: CAiSE 2015. LNCS, vol 9097. Springer, pp 349–366. https://doi.org/10.1007/978-3-319-19069-3_22
Bazhenova E, Weske M (2016) Deriving decision models from process models by enhanced decision mining. In: BPM 2015 workshops. Springer, pp 444–457. https://doi.org/10.1007/978-3-319-42887-1_36
Bazhenova E, Bülow S, Weske M (2016) Discovering decision models from event logs. In: BIS 2016. LNBIP, vol 255. Springer, pp 237–251. https://doi.org/10.1007/978-3-319-39426-8_19
Bazhenova E, Haarmann S, Ihde S, Solti A, Weske M (2017) Discovery of fuzzy DMN decision models from event logs. In: CAiSE 2017. LNCS, vol 10253. Springer, pp 629–647. https://doi.org/10.1007/978-3-319-59536-8_39
Bose JC, Mans RS, van der Aalst WMP (2013) Wanna improve process mining results? In: CIDM 2013. IEEE, pp 127–134. https://doi.org/10.1109/cidm.2013.6597227
Calvanese D, Dumas M, Ülari Laurson, Maggi FM, Montali M, Teinemaa I (2016) Semantics and analysis of DMN decision tables. In: BPM 2016. LNCS, vol 9850. Springer, pp 217–233. https://doi.org/10.1007/978-3-319-45348-4_13
de Leoni M, van der Aalst WMP (2013) Data-aware process mining: discovering decisions in processes using alignments. In: SAC 2013. ACM, pp 1454–1461. https://doi.org/10.1145/2480362.2480633
De Smedt J, vanden Broucke SKLM, Obregon J, Kim A, Jung JY, Vanthienen J (2017a) Decision mining in a broader context: an overview of the current landscape and future directions. In: BPM 2016 workshops. LNBIP, vol 281. Springer, pp 197–207. https://doi.org/10.1007/978-3-319-58457-7_15
De Smedt J, Hasic F, vanden Broucke S, Vanthienen J (2017b) Towards a holistic discovery of decisions in process-aware information systems. In: BPM 2017. LNCS, vol 10445. Springer. https://doi.org/10.1007/978-3-319-65000-5_11
DMN (2016) Decision Model and Notation (DMN) v1.1. http://www.omg.org/spec/DMN/1.1/
Kalenkova AA, de Leoni M, van der Aalst WMP (2014) Discovering, analyzing and enhancing BPMN models using prom. In: BPM 2014 Demos, CEUR-WS.org, CEUR workshop proceedings, vol 1295, p 36
Maggi FM, Dumas M, GarcÃa-Bañuelos L, Montali M (2013) Discovering data-aware declarative process models from event logs. In: BPM 2013. LNCS, vol 8094. Springer, pp 81–96. https://doi.org/10.1007/978-3-642-40176-3_8
Mannhardt F (2018) Multi-perspective process mining. PhD thesis, Eindhoven University of Technology
Mannhardt F, de Leoni M, Reijers HA (2015) The multi-perspective process explorer. In: BPM 2015 Demos, CEUR-WS.org, CEUR workshop proceedings, vol 1418, pp 130–134
Mannhardt F, de Leoni M, Reijers HA, van der Aalst WMP (2016) Decision mining revisited – discovering overlapping rules. In: CAiSE 2016. LNCS, vol 9694. Springer, pp 377–392. https://doi.org/10.1007/978-3-319-39696-5_23
Mannhardt F, de Leoni M, Reijers HA, van der Aalst WMP (2017) Data-driven process discovery – revealing conditional infrequent behavior from event logs. In: CAiSE 2017. LNCS, vol 10253, pp 545–560. https://doi.org/10.1007/978-3-319-59536-8_34
Rhodes A et al (2017) Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med 43(3):304–377. https://doi.org/10.1007/s00134-017-4683-6
Rosca D, Wild C (2002) Towards a flexible deployment of business rules. Expert Syst Appl 23(4):385–394. https://doi.org/10.1016/s0957-4174(02)00074-x
Rozinat A (2010) Process mining: conformance and extension. PhD thesis, Eindhoven University of Technology, Eindhoven
Rozinat A, van der Aalst WMP (2006) Decision mining in ProM. In: BPM 2006. LNCS, vol 4102. Springer, pp 420–425. https://doi.org/10.1007/11841760_33
Schönig S, Di Ciccio C, Maggi FM, Mendling J (2016) Discovery of multi-perspective declarative process models. In: ICSOC 2016. LNCS, vol 9936. Springer, pp 87–103. https://doi.org/10.1007/978-3-319-46295-0_6
van der Aalst WMP (2016) Process mining – data science in action, 2nd edn. Springer. https://doi.org/10.1007/978-3-662-49851-4
van Dongen BF, de Medeiros AKA, Verbeek HMW, Weijters AJMM, van der Aalst WMP (2005) The prom framework: a new era in process mining tool support. In: ICATPN 2005. LNCS, vol 3536. Springer, pp 444–454
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Leoni, M.d., Mannhardt, F. (2019). Decision Discovery in Business Processes. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_96
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