Definitions
Hierarchical process discovery is a family of methods in process mining that starting from an event log focuses on the automated discovery of process models containing one or more sub-processes, also known as hierarchical process models.
Overview
With the increasing availability of business process execution data, i.e., event logs, the use of automated process discovery techniques is becoming a common practice among business process management practitioners as a mean to quickly gain insights about the execution of a business process.
Despite several automated process discovery techniques had been proposed over the years (van der Aalst et al. 2004; Weijters and Ribeiro 2011; Leemans et al. 2013), these techniques fall short when dealing with event logs of processes containing sub-processes. When dealing with this type of event logs, classical automated process discovery often produces flat process models that are...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bauckmann J, Leser U, Naumann F (2010) Efficient and exact computation of inclusion dependencies for data integration. Technical report 34, Hasso-Plattner-Institute
Bose RPJC, van der Aalst WMP (2009) Abstractions in process mining: a taxonomy of patterns. In: Proceedings of the 7th international conference on business process management. Lecture notes in computer science, vol 5701. Springer, pp 159–175
Conforti R, Dumas M, GarcÃa-Bañuelos L, La Rosa M (2014) Beyond tasks and gateways: discovering BPMN models with subprocesses, boundary events and activity markers. In: Proceedings of the 12th international conference on business process management. Lecture notes in computer science, vol 8659. Springer, pp 101–117
Conforti R, Augusto A, Rosa ML, Dumas M, GarcÃa-Bañuelos L (2016a) BPMN miner 2.0: discovering hierarchical and block-structured BPMN process models. In: Proceedings of the BPM demo track 2016 co-located with the 14th international conference on business process management, CEUR-WS.org, CEUR workshop proceedings, vol 1789, pp 39–43
Conforti R, Dumas M, GarcÃa-Bañuelos L, La Rosa M (2016b) BPMN miner: automated discovery of BPMN process models with hierarchical structure. Inf Syst 56:284–303
Huhtala Y, Kärkkäinen J, Porkka P, Toivonen H (1999) TANE: an efficient algorithm for discovering functional and approximate dependencies. Comput J 42(2):100–111
Leemans SJJ, Fahland D, van der Aalst WMP (2013) Discovering block-structured process models from event logs – a constructive approach. In: Proceedings of the 34th international conference on application and theory of petri nets and concurrency. Lecture notes in business information processing, vol 7927. Springer, pp 311–329
Li J, Bose RPJC, van der Aalst WMP (2011) Mining context-dependent and interactive business process maps using execution patterns. In: Proceedings of business process management workshops. Lecture notes in business information processing, vol 66. Springer, pp 109–121
Maggi FM, Slaats T, Reijers HA (2014) The automated discovery of hybrid processes. In: Proceedings of the 12th international conference on business process management. Lecture notes in computer science, vol 8659. Springer, pp 392–399
Object Management Group (OMG) (2011) Business process model and notation (BPMN) ver. 2.0. Object Management Group (OMG). http://www.omg.org/spec/BPMN/2.0
Sun Y, Bauer B (2016) A graph and trace clustering-based approach for abstracting mined business process models. In: Proceedings of the 18th international conference on enterprise information systems, SciTePress, pp 63–74
van der Aalst WMP, Weijters T, Maruster L (2004) Workflow mining: discovering process models from event logs. IEEE Trans Know Data Eng 16(9):1128–1142
Wang Y, Wen L, Yan Z, Sun B, Wang J (2015) Discovering BPMN models with sub-processes and multi-instance markers. In: Proceedings of the on the move (OTM) confederated international conferences. Lecture notes in computer science, vol 9415. Springer, pp 185–201
Weber I, Farshchi M, Mendling J, Schneider J (2015) Mining processes with multi-instantiation. In: Proceedings of the 30th annual ACM symposium on applied computing. ACM, pp 1231–1237
Weijters AJMM, Ribeiro JTS (2011) Flexible heuristics miner (FHM). In: Proceedings of the IEEE symposium on computational intelligence and data mining. IEEE, pp 310–317
Zhang M, Hadjieleftheriou M, Ooi BC, Procopiuc CM, Srivastava D (2010) On multi-column foreign key discovery. Proc VLDB Endow 3(1):805–814
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this entry
Cite this entry
Conforti, R. (2019). Hierarchical Process Discovery. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_94
Download citation
DOI: https://doi.org/10.1007/978-3-319-77525-8_94
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77524-1
Online ISBN: 978-3-319-77525-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering