Professor Marcello La Rosa
- Business Intelligence
- Business Process Management
- Information Systems
- Process Mining
Professor Marcello La Rosa leads the Information Systems group within the School of Computing and Information Systems at The University of Melbourne, where he also serves as the Deputy Head of School for Engagement. Prior to 2018, 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 250 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.
- Leno, V.; Dumas, M.; Maggi, FM.; La Rosa, M.; Polyvyanyy, A. Automated discovery of declarative process models with correlated data conditions. Information Systems. Elsevier BV. 2020, Vol. 89, pp. 101482-101482. DOI: 10.1016/j.is.2019.101482
- Bayomie, D.; Di Ciccio, C.; La Rosa, M.; Mendling, J. A probabilistic approach to event-case correlation for process mining. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer International Publishing. 2019, Vol. 11788 LNCS, pp. 136-152. DOI: 10.1007/978-3-030-33223-5_12
- Leno, V.; Polyvyanyy, A.; La Rosa, M.; Dumas, M.; Maggi, FM. Action logger: Enabling process mining for robotic process automation. CEUR Workshop Proceedings. CEUR Workshop Proceedings. 2019, Vol. 2420.
- 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. 2019, Vol. 31, Issue 4, pp. 686-705. DOI: 10.1109/TKDE.2018.2841877
- La Rosa, M.; Plebani, P.; Reichert, M. CAiSE 2019 doctoral consortium foreword. CEUR Workshop Proceedings. 2019, Vol. 2370.
- Van Zelst, SJ.; Fani Sani, M.; Ostovar, A.; Conforti, R.; La Rosa, M. Detection and removal of infrequent behavior from event streams of business processes. Information Systems. Elsevier BV. 2019, pp. 101451-101451. DOI: 10.1016/j.is.2019.101451
- Bosco, A.; Augusto, A.; Dumas, M.; La Rosa, M.; Fortino, G. Discovering Automatable Routines From User Interaction Logs. . Springer International Publishing. 2019, Vol. 360, pp. 144-162. DOI: 10.1007/978-3-030-26643-1_9
- 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. DOI: 10.1145/3289181
- Carmona, J.; Jans, M.; La Rosa, M. Message from the program chairs. 2019 International Conference on Process Mining (ICPM). IEEE. 2019, pp. IX-. DOI: 10.1109/ICPM.2019.00006
- Augusto, A.; Dumas, M.; La Rosa, M. Metaheuristic Optimization for Automated Business Process Discovery. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2019, Vol. 11675 LNCS, pp. 268-285. DOI: 10.1007/978-3-030-26619-6_18
- Verenich, I.; Dumas, M.; La Rosa, M.; Nguyen, H. Predicting process performance: A white‐box approach based on process models. Journal of Software: Evolution and Process. Wiley. 2019, Vol. 31, Issue 6. DOI: 10.1002/smr.2170
- 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 LTD. 2019, Vol. 59, Issue 2, pp. 251-284. DOI: 10.1007/s10115-018-1214-x
- Nguyen, H.; Dumas, M.; Ter Hofstede, A.; La Rosa, M.; Maggi, FM. Stage-based Discovery of Business Process Models from Event Logs. Information Systems. Elsevier. 2019, Vol. 84, pp. 214-237. DOI: 10.1016/j.is.2019.05.002
- 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. ACM Transactions on Intelligent Systems and Technology. Association for Computing Machinery. 2019, Vol. 10, Issue 4. DOI: 10.1145/3331449
- Migliorini, S.; Gambini, M.; Combi, C.; La Rosa, M. The Rise of Enforceable Business Processes from the Hashes of Blockchain-Based Smart Contracts. Lecture Notes in Business Information Processing. Springer. 2019, Vol. 352, pp. 130-138. DOI: 10.1007/978-3-030-20618-5_9
- Augusto, A.; Armas Cervantes, A.; Conforti, R.; Dumas, M.; La Rosa, M.; Reissner, D. Abstract and Compare: A Framework for Defining 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
- 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. 2018, Vol. 117, pp. 373-392. DOI: 10.1016/j.datak.2018.04.007
- 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. Association for Computing Machinery (ACM). 2018, Vol. 9, Issue 1, pp. 1-16. DOI: 10.1145/3183367
- 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
- Garcia-banuelos, L.; Van Beest, NRTP.; Dumas, M.; La Rosa, M.; Mertens, W. Complete and Interpretable Conformance Checking of Business Processes. IEEE Transactions on Software Engineering. IEEE COMPUTER SOC. 2018, Vol. 44, Issue 3, pp. 262-290. DOI: 10.1109/TSE.2017.2668418
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