Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Hierarchical Process Discovery

  • Raffaele ConfortiEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_94

Synonyms

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...
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References

  1. Bauckmann J, Leser U, Naumann F (2010) Efficient and exact computation of inclusion dependencies for data integration. Technical report 34, Hasso-Plattner-InstituteGoogle Scholar
  2. 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 159–175Google Scholar
  3. Conforti R, Dumas M, García-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 101–117Google Scholar
  4. Conforti R, Augusto A, Rosa ML, Dumas M, García-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 39–43Google Scholar
  5. Conforti R, Dumas M, García-Bañuelos L, La Rosa M (2016b) BPMN miner: automated discovery of BPMN process models with hierarchical structure. Inf Syst 56:284–303CrossRefGoogle Scholar
  6. Huhtala Y, Kärkkäinen J, Porkka P, Toivonen H (1999) TANE: an efficient algorithm for discovering functional and approximate dependencies. Comput J 42(2):100–111zbMATHCrossRefGoogle Scholar
  7. 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 311–329Google Scholar
  8. 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 109–121Google Scholar
  9. 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 392–399Google Scholar
  10. 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
  11. 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 63–74Google Scholar
  12. van der Aalst WMP, Weijters T, Maruster L (2004) Workflow mining: discovering process models from event logs. IEEE Trans Know Data Eng 16(9):1128–1142CrossRefGoogle Scholar
  13. 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 185–201Google Scholar
  14. 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 1231–1237Google Scholar
  15. Weijters AJMM, Ribeiro JTS (2011) Flexible heuristics miner (FHM). In: Proceedings of the IEEE symposium on computational intelligence and data mining. IEEE, pp 310–317Google Scholar
  16. Zhang M, Hadjieleftheriou M, Ooi BC, Procopiuc CM, Srivastava D (2010) On multi-column foreign key discovery. Proc VLDB Endow 3(1):805–814CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of MelbourneMelbourneAustralia