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. Prior to that, he was a Professor with 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).
Marcello leads the Apromore Initiative, a strategic inter-university collaboration for the development of an open-source process analytics platform. 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.
- 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
- 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
- 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. DOI: 10.1145/3183367
- 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. IEEE - Institute of Electrical and Electronic Engineers. 2018, Vol. 44, Issue 3. DOI: 10.1109/TSE.2017.2668418
- Van Zelst SJ, Fani Sani M, 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 Verlag. 2018, Vol. 10816 LNCS. DOI: 10.1007/978-3-319-91563-0_3
- Dumas M, La Rosa M, Mendling J, Reijers HA. Fundamentals of business process management: Second Edition. Fundamentals of Business Process Management: Second Edition. 2018. DOI: 10.1007/978-3-662-56509-4
- Verenich I, MÕŠkovski S, Raboczi S, Dumas M, La Rosa M, Maggi FM. Predictive process monitoring in apromore. International Conference on Advanced Information Systems Engineering (CAiSE). 2018, Vol. 317. DOI: 10.1007/978-3-319-92901-9_21
- 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
- La Rosa M, Van Der Aalst WMP, Dumas M, Milani FP. Business process variability modeling: A survey. ACM Computing Surveys. Association for Computing Machinery. 2017, Vol. 50, Issue 1. DOI: 10.1145/3041957
- Ostovar A, Maaradji A, La Rosa M, Ter Hofstede AHM. Characterizing drift from event streams of business processes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10253 LNCS. DOI: 10.1007/978-3-319-59536-8_14
- Fornari F, Gnesi S, La Rosa M, Polini A, Re B, Spagnolo GO. Checking business process modeling guidelines in apromore. CEUR Workshop Proceedings. Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V. 2017, Vol. 1920.
- Maaradji A, Dumas M, La Rosa M, Ostovar A. Detecting sudden and gradual drifts in business processes from execution traces. IEEE Transactions on Knowledge and Data Engineering. IEEE Computer Society. 2017, Vol. 29, Issue 10. DOI: 10.1109/TKDE.2017.2720601
- Hompes BFA, Maaradji A, La Rosa M, Dumas M, Buijs JCAM, Van Der Aalst WMP. Discovering causal factors explaining business process performance variation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2017, Vol. 10253 LNCS. DOI: 10.1007/978-3-319-59536-8_12
- 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
- Armas-Cervantes A, Van Beest NRTP, La Rosa M, Dumas M, Raboczi S. Incremental and interactive business process model repair in apromore. CEUR Workshop Proceedings. Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V. 2017, Vol. 1920.
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