Massimiliano de Leoni

Dr. Massimiliano de Leoni is assistant professor at the PA group. His research focuses in the areas of Process-aware Information Systems and Business Process Management, predominantly on multi-perspective process mining, process-aware decision support systems as well as on visualization techniques for business process management and analysis. He has a genuine interest in the practical applications of his research in real-life settings, which led him to concretely develop his ideas in term of software tools and apply them with a large number of organizations world-wide.

Position: UD
Room: MF 7.059
Tel (internal): 8430
Links: Personal home page
Google scholar page
Scopus page
ORCID page
DBLP page
TU/e employee page

Courses

Master Projects

Projects

  • Core - CORE – Consistently Optimised REsilient secure global supply-chains Description The CORE project aims to produce cost effective, fast and robust solutions for worldwide Global Supply Chain system. The project will implement an ecosystem where interoperability, security, resilience and real-time are optimized. The role of the AIS group in the project is to employ process mining Read More ...

Publications

  • What if process predictions are not followed by good recommendations? - Dees, M., de Leoni, M., van der Aalst, W. M. P., & Reijers, H. A. (2019). What if process predictions are not followed by good recommendations? In J. vom Brocke , J. Mendling, & M. Rosemann (Eds.), 17th International Conference on Business Process Management 2019 Industry Forum: Proceedings of the Industry Forum at BPM 2019 Read More ...
  • A framework to evaluate and compare decision-mining techniques - Jouck, T., de Leoni, M., & Depaire, B. (2019). A framework to evaluate and compare decision-mining techniques. In F. Daniel, Q. Z. Sheng, & H. Motahari (Eds.), Business Process Management Workshops – BPM 2018 International Workshops, Revised Papers (pp. 482-493). (Lecture Notes in Business Information Processing; Vol. 342). Cham: Springer. DOI: 10.1007/978-3-030-11641-5_38 Abstract During the Read More ...
  • Alarm-based prescriptive process monitoring - Teinemaa, Irene, Tax, Niek, de Leoni, Massimiliano, Dumas, Marlon & Maggi, Fabrizio Maria (2018). Alarm-based prescriptive process monitoring. In Ingo Weber, Jan vom Brocke, Marco Montali & Mathias Weske (Eds.), Business Process Management Forum – BPM Forum 2018, Proceedings (pp. 91-107). (Lecture Notes in Business Information Processing, No. 329). Springer. Abstract Predictive process monitoring is Read More ...
  • Aligning partially-ordered process-execution traces and models using automated planning - de Leoni, Massimiliano, Lanciano, Giacomo & Marrella, Andrea (2018). Aligning partially-ordered process-execution traces and models using automated planning. 28th International Conference on Automated Planning and Scheduling, ICAPS 2018 (pp. 321-329). (Proceedings International Conference on Automated Planning and Scheduling, ICAPS). Abstract Conformance checking is the problem of verifying if the actual executions of business processes, which Read More ...
  • A holistic approach for soundness verification of decision-aware process models - de Leoni, Massimiliano, Felli, Paolo & Montali, Marco (2018). A holistic approach for soundness verification of decision-aware process models. In Xiaoyong Du, Guoliang Li, Zhanhuai Li, Juan C. Trujillo, Tok Wang Ling, Karen C. Davis & Mong Li Lee (Eds.), Conceptual Modeling – 37th International Conference, ER 2018, Proceedings (pp. 219-235). (Lecture Notes in Computer Read More ...
  • Discovering high-level BPMN process models from event data - Kalenkova, Anna, Burattin, Andrea, de Leoni, Massimiliano, van der Aalst, Wil & Sperduti, Alessandro (2019). Discovering high-level BPMN process models from event data. Business Process Management Journal, 25(5), 995-1019. DOI: 10.1108/BPMJ-02-2018-0051 Abstract Purpose: The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using Read More ...
  • Multi-perspective process mining - Mannhardt, F. (2018). Multi-perspective process mining. Eindhoven: Technische Universiteit Eindhoven. ((Co-)promot.: Hajo Reijers, Wil van der Aalst & Massimiliano de Leoni).
  • Time and activity sequence prediction of business process instances - Polato, Mirko, Sperduti, Alessandro, Burattin, Andrea & de Leoni, Massimiliano (2018). Time and activity sequence prediction of business process instances. Computing, 100(9), 1005-1031 Abstract The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to Read More ...
  • Guided Process Discovery : A Pattern-based Approach - Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P. & Toussaint, P. (2018). Guided Process Discovery : A Pattern-based Approach. Information Systems, 76, 1-18 Abstract Process mining techniques analyze processes based on events stored in event logs. Yet, low-level events recorded by information systems may not directly match high-level activities that make sense Read More ...
  • Process variant comparison: using event logs to detect differences in behavior and business rules - Bolt, A., de Leoni, M. & van der Aalst, W.M.P. (2018). Process variant comparison: using event logs to detect differences in behavior and business rules. Information Systems, 74(1), 53-66. Abstract This paper addresses the problem of comparing different variants of the same process. We aim to detect relevant differences between processes based on what was Read More ...

Presentations

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