Alessandro Gianola

Title: Modeling and Analysis of Data-Aware Business Processes

Abstract: Integrating data and processes to understand their concrete interplay is a long-standing problem in business process management (BPM). This problem becomes even more challenging when modeling Data-Aware (Business) Processes (DAPs) meets the need for their automatic analysis along their entire lifecycle, ranging from model-driven analysis at design time to data-driven analysis and process mining.
The frameworks studied in the literature for formalizing and analyzing DAPs present two main drawbacks. First, the results are quite abstract, with strong assumptions on the underlying models that do not match those of front-end languages used in practice. Second, the employed techniques, when concretely implemented, are developed ad hoc, without appealing to general-purpose automatic tools.
In this talk, I will present how model-driven analysis (in particular, safety checking) and data-driven analysis (in particular, conformance checking) can be solved by using successful and well-established techniques from AI. I will mainly focus on the results that I obtained in the last years: formal verification and conformance checking for DAPs can be attacked via symbolic reasoning developed in the realm of infinite-state model checking, which provides not only solid foundations, but also highly efficient technologies, such as those stemming from SMT solvers. Finally, I will discuss the impact of DAPs analysis both in the Information Systems and AI communities, as well as in the industry, and I will report on the open challenges to address in the future in my research project.'

Biography: Alessandro Gianola is a Postdoctoral Researcher in Computer Science at the Free University of Bozen-Bolzano (Italy), where he earned cum laude a PhD in Computer Science. He was Visiting Scholar at the Department of Computer Science and Engineering of the University of California San Diego (UCSD), USA. He works on AI and Business Process Management (BPM): his research focuses on formal methods for the modeling and analysis of complex business processes enriched with real data, in the context of BPM and process mining. The variety of his research interests is witnessed by the wide range of scientific venues where he publishes, including top-tier journals such as Information Systems, the Journal of Automated Reasoning, and Software and Systems Modeling, and premier international conferences such as BPM, AAAI, IJCAI and IJCAR. Specifically, he authored 36 referred papers: 9 journal articles, 3 papers in A* conferences and 7 in A conferences, including 5 papers at the main track of the BPM conference.  He is recipient of two best paper awards (PRIMA 2020 and BPM 2021). His PhD dissertation has been accepted to be published as a Springer monograph in the LNBIP series, and won two prestigious awards: the 2022 Best Italian PhD Thesis in Theoretical Computer Science Award given by the Italian Chapter of EATCS, and the 2022 Best BPM Dissertation Award given by the BPM Association. He is a member of the Program Committee of BPM 2023 and an organizer of the First International Workshop on Formal Methods for Business Process Management, co-located with BPM 2023. He was Invited Speaker at iPRA 2022, co-located with FLoC 2022 (the 8th Federated Logic Conference), on the topic of data-aware processes verification.