Dr Raffaele Conforti

  • Room: Level: 10 Room: 09
  • Building: Doug McDonell Building
  • Campus: Parkville

Personal webpage

www.raffaeleconforti.com

Biography

Raffaele Conforti is a Lecturer in the area of Business Process Management (BPM) with the School of Computing and Information Systems. Before joining the University of Melbourne, he was a Postdoctoral Research Fellow within the Business Process Management Discipline in the Queensland University of Technology, from which he also holds a PhD. His research spans across the wide spectrum of business process management with a particular interest to process mining. In the area of process mining, his interest focuses on the development of techniques for process discovery, noise filtering, and conformance checking. He is also involved in the Apromore initiative, an advanced process analytics platform, where in the past he played the role of chief architect.

Recent publications

  1. Augusto A, Conforti R, Dumas M, La Rosa M, Maggi FM, Marrella A, Mecella M, Soo A. Automated Discovery of Process Models from Event Logs: Review and Benchmark. IEEE Transactions on Knowledge and Data Engineering. IEEE Computer Society. 2018. DOI: 10.1109/TKDE.2018.2841877
  2. Augusto A, Conforti R, Dumas M, La Rosa M, Bruno G. Automated discovery of structured process models from event logs: The discover-and-structure approach. Data and Knowledge Engineering. Elsevier. 2018. DOI: 10.1016/j.datak.2018.04.007
  3. Van Zelst SJ, Sani MF, Ostovar A, Conforti R, La Rosa M. Filtering Spurious Events from Event Streams of Business Processes. 30th International Conference on Advanced Information Systems Engineering. Springer. 2018. Editors: Krogstie J, Reijers HA.
  4. Augusto A, Conforti R, Dumas M, La Rosa M, Polyvyanyy A. Split miner: automated discovery of accurate and simple business process models from event logs. Knowledge and Information Systems. Springer London. 2018. DOI: 10.1007/s10115-018-1214-x
  5. Conforti R, La Rosa M, Ter Hofstede AHM. Filtering Out Infrequent Behavior from Business Process Event Logs. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. IEEE Computer Society. 2017, Vol. 29, Issue 2. DOI: 10.1109/TKDE.2016.2614680
  6. Reißner D, Conforti R, Dumas M, La Rosa M, Armas-Cervantes A. Scalable conformance checking of business processes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10573 LNCS. DOI: 10.1007/978-3-319-69462-7_38
  7. Augusto A, Conforti R, Dumas M, La Rosa M. Split miner: Discovering accurate and simple business process models from event logs. Proceedings - IEEE International Conference on Data Mining, ICDM. 2017, Vol. 2017-November. DOI: 10.1109/ICDM.2017.9
  8. Augusto A, Conforti R, Dumas M, La Rosa M, Bruno G. Automated discovery of structured process models: Discover structured vs. Discover and structure. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2016, Vol. 9974 LNCS. DOI: 10.1007/978-3-319-46397-1_25
  9. Conforti R, Augusto A, La Rosa M, Dumas M, GarcÍa-BaÑuelos L. BPMN miner 2.0: Discovering hierarchical and block-structured BPMN process models. CEUR Workshop Proceedings. Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V. 2016, Vol. 1789.
  10. Conforti R, Dumas M, Garcia-Banuelos L, La Rosa M. BPMN Miner: Automated discovery of BPMN process models with hierarchical structure. INFORMATION SYSTEMS. Pergamon. 2016, Vol. 56. DOI: 10.1016/j.is.2015.07.004
  11. Conforti R, Fink S, Manderscheid J, RÖglinger M. PRISM - A predictive risk monitoring approach for business processes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2016, Vol. 9850 LNCS. DOI: 10.1007/978-3-319-45348-4_22
  12. Conforti R, De Leoni M, La Rosa M, Van Der Aalst WMP, Ter Hofstede AHM. A recommendation system for predicting risks across multiple business process instances. DECISION SUPPORT SYSTEMS. Elsevier BV. 2015, Vol. 69. DOI: 10.1016/j.dss.2014.10.006
  13. Conforti R, Dumas M, La Rosa M, Maaradji A, Nguyen HH, Ostovar A, Raboczi S. Analysis of business process variants in apromore. CEUR Workshop Proceedings. Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V. 2015, Vol. 1418.
  14. Leontjeva A, Conforti R, Di Francescomarino C, Dumas M, Maggi FM. Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes. 13th International Conference on Business Process Management (BPM). Springer Verlag. 2015, Vol. 9253. Editors: Motaharinezhad HR, Recker J, Weidlich M. DOI: 10.1007/978-3-319-23063-4_21
  15. Polyvyanyy A, Corno L, Conforti R, Raboczi S, La Rosa M, Fortino G. Process querying in apromore. CEUR Workshop Proceedings. Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V. 2015, Vol. 1418.

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile