Predictive process mining aims to provide information about the future evolution of processes, given the information available from past process executions and about the current state. In this paper, we focus on judicial trials as a specific type of process and we analyse the aspects that have an impact on process duration and the challenges posed by this type of analysis. In particular, we focus on analyzing and discussing the factors that have most impact on the duration of trials. An analysis framework is proposed, which takes advantage of a large dataset describing five years of trials in the Court of Appeal of Milan. We examine both the phases and total length of the trials and we propose techniques to identify events that are potentially critical, as they have a major impact on their duration.

Variants Analysis in Judicial Trials: Challenges and Initial Results

Campi, Alessandro;Ceri, Stefano;Dilettis, Marco;Pernici, Barbara
2025-01-01

Abstract

Predictive process mining aims to provide information about the future evolution of processes, given the information available from past process executions and about the current state. In this paper, we focus on judicial trials as a specific type of process and we analyse the aspects that have an impact on process duration and the challenges posed by this type of analysis. In particular, we focus on analyzing and discussing the factors that have most impact on the duration of trials. An analysis framework is proposed, which takes advantage of a large dataset describing five years of trials in the Court of Appeal of Milan. We examine both the phases and total length of the trials and we propose techniques to identify events that are potentially critical, as they have a major impact on their duration.
2025
Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023
9783031746291
9783031746307
Data mining
judiciary
Process mining
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1285327
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