Professor Marcello La Rosa

  • Room: Level: 10 Room: 10.20
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Business Intelligence
  • Business Process Management
  • Information Systems
  • Process Mining

Personal webpage

http://marcellolarosa.com

Biography

Professor Marcello La Rosa leads the Information Systems group within the School of Computing and Information Systems at The University of Melbourne, and serves as the Director of Engagement for his School. Prior to that, he was a Professor at the Queensland University of Technology, where he led the Business Process Management Discipline (2016-17) and served as the Academic Director for corporate programs and partnerships (2012-17). He was also the recipient of an Information Systems Fellowship from the University of Liechtenstein and held a part-time Principal Researcher position at NICTA (now Data61).

Marcello attracted research funds in excess of AUD 6.5M from nationally competitive grant schemes and private organisations. His research interests span different Business Process Management areas with a focus on process mining and business intelligence, process consolidation and automation, in which he published over 100 papers, including articles in outlets such as ACM Transactions on Software Engineering and Methodology, ACM Computing Surveys, IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering, Journal of Systems and Software, Information Systems and Decision Support Systems. He obtained best paper awards at the Int. Conference on Business Process Management (BPM'13), Int. Conference on Conceptual Modeling (ER'16) and Int. Conference on Software and System Process (ICSSP'17), and a distinguished paper award at the Int. Conference on Advanced Information Systems Engineering (CAiSE'18), as well as two best demonstration paper awards (BPM'17 and CAiSE'18).

Marcello is the driving force behind the Apromore Initiative, a strategic inter-university collaboration for the development of an open-source process analytics platform, which led to significant technology transfer. He has taught BPM to practitioners and students in Australia and overseas for over ten years. Based on this experience, he co-authored the textbook "Fundamentals of Business Process Management" (Springer, 2nd edition), through which he influenced the curriculum of over 200 universities in the world. Using this book, Marcello co-developed a series of MOOCs (Massive Open Online Courses) on the subject, which have attracted over 25,000 participants to date.

Recent publications

  1. Armas-cervantes, A.; Dumas, M.; La Rosa, M.; Maaradji, A. Local concurrency detection in business process event logs. ACM Transactions on Internet Technology. Association for Computing Machinery. 2019, Vol. 19, Issue 1, pp. 1-23. DOI: 10.1145/3289181
  2. Augusto, A.; Armas-cervantes, A.; Conforti, R.; Dumas, M.; La Rosa, M.; Reissner, D. Abstract-and-compare: A family of scalable precision measures for automated process discovery. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). SpringerLink. 2018, Vol. 11080 LNCS, pp. 158-175. DOI: 10.1007/978-3-319-98648-7_10
  3. 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. Institute of Electrical and Electronics Engineers. 2018, Vol. 31, Issue 4, pp. 686-705. DOI: 10.1109/TKDE.2018.2841877
  4. 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 & Knowledge Engineering. ELSEVIER SCIENCE BV. 2018, Vol. 117, pp. 373-392. DOI: 10.1016/j.datak.2018.04.007
  5. Mendling, J.; Weber, I.; Van Der Aalst, W.; Brocke, JV.; Cabanillas, C.; Daniel, F.; Debois, S.; Di Ciccio, C.; Dumas, M.; Dustdar, S.; Gal, A.; García-bañuelos, L.; Governatori, G.; Hull, R.; La Rosa, M.; Leopold, H.; Leymann, F.; Recker, J.; Reichert, M.; Reijers, HA.; Rinderlema, S.; Solti, A.; Rosemann, M.; Schulte, S.; Singh, MP.; Slaats, T.; Staples, M.; Weber, B.; Weidlich, M.; Weske, M.; Xu, X.; Zhu, L. Blockchains for business process management - Challenges and opportunities. ACM Transactions on Management Information Systems. 2018, Vol. 9, Issue 1, pp. 1-16. DOI: 10.1145/3183367
  6. Fornari, F.; La Rosa, M.; Polini, A.; Re, B.; Tiezzi, F. Checking business process correctness in apromore. Lecture Notes in Business Information Processing. SpringerLink. 2018, Vol. 317, pp. 114-123. DOI: 10.1007/978-3-319-92901-9_11
  7. García-bañuelos, L.; Van Beest, NRTP.; Dumas, M.; La Rosa, M.; Mertens, W. Complete and Interpretable Conformance Checking of Business Processes. IEEE Transactions on Software Engineering. 2018, Vol. 44, Issue 3, pp. 262-290. DOI: 10.1109/TSE.2017.2668418
  8. Leno, V.; Armas-cervantes, A.; Dumas, M.; La Rosa, M.; Maggi, FM. Discovering process maps from event streams. Proceedings of the 2018 International Conference on Software and System Process - ICSSP '18. Association for Computing Machinery (ACM). 2018, pp. 86-95. DOI: 10.1145/3202710.3203154
  9. Van Zelst, SJ.; Fani Sani, M.; Ostovar, A.; Conforti, R.; La Rosa, M. Filtering spurious events from event streams of business processes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2018, Vol. 10816 LNCS, pp. 35-52. DOI: 10.1007/978-3-319-91563-0_3
  10. La Rosa, M.; Mendling, J. Fundamentals of business process management: Fifty years of BPM teaching distilled. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Nature. 2018, Vol. 10816 LNCS, pp. 623-.
  11. Dumas, M.; La Rosa, M.; Mendling, J.; Reijers, HA. Fundamentals of business process management: Second Edition. . Springer-Verlag. 2018, pp. 1-527. DOI: 10.1007/978-3-662-56509-4
  12. Nguyen, H.; Dumas, M.; La Rosa, M.; Ter Hofstede, AHM. Multi-perspective comparison of business process variants based on event logs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). SpringerLink. 2018, Vol. 11157 LNCS, pp. 449-459. DOI: 10.1007/978-3-030-00847-5_32
  13. Leno, V.; Dumas, M.; Maggi, FM.; La Rosa, M. Multi-Perspective process model discovery for robotic process automation. CEUR Workshop Proceedings. CEUR Workshop Proceedings. 2018, Vol. 2114, pp. 37-45.
  14. Verenich, I.; Dumas, M.; La Rosa, M.; Nguyen, H.; Ter Hofstede, A. Predicting process performance: A white-box approach. . 2018.
  15. Verenich, I.; Mõškovski, S.; Raboczi, S.; Dumas, M.; La Rosa, M.; Maggi, FM. Predictive process monitoring in apromore. Lecture Notes in Business Information Processing. Springer-Verlag, Journals. 2018, Vol. 317, pp. 244-253. DOI: 10.1007/978-3-319-92901-9_21
  16. Ostovar, A.; Leemans, SJJ.; La Rosa, M. Robust Drift Characterization from Event Streams of Business Processes. . Association for Computing Machinery. 2018.
  17. 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 Verlag. 2018, Vol. 59, Issue 2, pp. 1-34. DOI: 10.1007/s10115-018-1214-x
  18. Verenich, I.; Dumas, M.; La Rosa, M.; Maggi, F.; Teinemaa, I. Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. . 2018.
  19. Conforti, R.; La Rosa, M.; Ter Hofstede, A. Timestamp Repair for Business Process Event Logs. . 2018.
  20. La Rosa, M.; Van Der Aalst, WMP.; Dumas, M.; Milani, FP. Business process variability modeling: A survey. ACM Computing Surveys. 2017, Vol. 50, Issue 1, pp. 1-45. DOI: 10.1145/3041957

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