Extensive research has explored business process model similarity. This work aims to explore the coherence and structure of the existing studies, with a focus on evaluating behavioural similarity. We conducted a systematic review of the literature on process model similarity, with a focus on two main measurement approaches: trace-based and model-based similarity. Based on our review of 99 papers, we developed a three-dimensional framework and conducted a quantitative comparison of selected similarity measures to deepen our analysis. Our findings provide valuable insights that could enhance the assessment of process model similarity, particularly with an emphasis on behaviour. The review process followed a six-phase systematic methodology, from the identification of relevant keywords to the creation of bibliographic maps for a visual representation of the findings. these insights serve as a foundation for future research and practical applications within the field.

Model repair techniques aim at automatically updating a process model to incorporate behaviors that are observed in reality but are not compliant with the original model. Most state-of-the-art techniques focus on the fitness of the repaired models, with the goal of including single anomalous behaviors observed in a log in the form of the events. This often hampers the precision of the obtained models, which end up allowing much more behaviors than intended. In the quest of techniques avoiding this over-generalization pitfall, some notion of higher-level anomalous structure is taken into account. The type of structure considered is however typically limited to sequences of low-level events. In this work, we introduce a novel repair approach targeting more general high-level anomalous structures. To do this, we exploit instance graph representations of anomalous behaviors, that can be derived from the event log and the original process model. Our experiments show that considering high-level anomalies allows to generate repaired models that incorporate the behaviors of interest while maintaining precision and simplicity closer to the original model.

Abstract The rise of e-learning systems has revolutionized education, enabling the collection of valuable students’ activity data for continuous improvement. While existing studies have predominantly focused on prolonged learning paths, short-term tutorials offer a flexible and efficient alternative that is recently gaining increasing popularity. This article presents a methodology for investigating e-learning systems for short-term tutorials leveraging user behavior tracking and process mining techniques. A case study involving a web-based tutorial with approximately one hour of learning explores the learning processes of 250 students in Italy. The study analyzes learning outcomes and investigates the impact of different learning paths on student progress. The research questions concern i) the extraction of activity flows in short-term tutorials; ii) the prediction of outcomes in the early stages of short-term learning process. The proposed approach provides descriptive insights into the learning process which can also be used to offer prescriptive guidance.

Predictive monitoring of business processes aims at predicting the future of an ongoing process execution. In this work, we focus on the prediction of the next activities to be executed in a running case. However, in contrast with most state-of-the-art approaches, focused on predicting exactly the next activity that will be executed from the current state of the process, we propose an approach aimed at predicting the portion of the process (or “location”) that is likely to be executed next. The notion of location allows us to detect activities belonging to the same portion of a control-flow construct (e.g., at the beginning of a parallelism, or at the end of a loop). It provides an abstraction mechanism from the level of the single activity, which can be used to provide the process analyst with an higher-level overview of what can be expected next in the process execution. We validated the approach over a set of real-world datasets comparing and discussing different strategies for training a classifier in returning a location in place of an activity label.

Outcome-oriented predictive process monitoring aims at classifying a running process execution according to a given set of categorical outcomes, leveraging data on past process executions. Most previous studies employ Recurrent Neural Networks to encode the sequence of events, without taking the structure of the process into account. However, process executions typically involve complex control-flow constructs, like parallelism and loops. Different executions of these constructs can be recorded as different event sequences in the event log. This makes it challenging for a recurrent classifier to detect potential relations between a high-level control-flow construct and the prediction target. This is especially true in the presence of high variability in process executions and lack of data. In this paper, we propose a novel approach which encodes the control-flow construct each event belongs to. First, we exploit Local Process Model mining techniques to extract frequently occurring control-flow patterns from the event log. Then, we employ different encoding techniques to enrich an on-going process execution with information related to the extracted control-flow patterns. We tested the proposed method on nine real-life event logs. The obtained results show consistent improvements in the prediction performance.

Stomach and esophageal cancer are in the top ten most common cancers worldwide, both with high mortality rate. Approximately one-third of these patients have metastases at initial diagnosis and should receive personalized palliative care to improve their remaining life time. However, there is a lack of consensus about personalized palliative care options. This often leads to difficulties in determining the right treatment pathway for individual patients. This study investigates the application of process mining techniques on palliative care pathways for stomach and esophageal cancer to obtain an evidence-based understanding of which palliative treatments are commonly carried out in clinical practice and how they are associated with patients’ survival time. Given the high variability of the treatment pathways, ‘local models’ are derived, rather than end-to-end process models, which are then validated with the aid of physicians. In addition, this study also investigates the use of predictive process monitoring techniques to predict patients’ life expectancy. The results show the benefit of taking the process-flow into account in predicting the outcome of the palliative treatments.

Conformance checking allows organizations to compare process executions recorded by the IT system against a process model representing the normative behavior. Most of the existing techniques, however, are only able to pinpoint where individual process executions deviate from the normative behavior, without considering neither possible correlations among occurred deviations nor their frequency. Moreover, the actual control-flow of the process is not taken into account in the analysis. Neglecting possible parallelisms among process activities can lead to inaccurate diagnostics; it also poses some challenges in interpreting the results, since deviations occurring in parallel behaviors are often instantiated in different sequential behaviors in different traces. In this work, we present an approach to extract anomalous frequent patterns from historical logging data. The extracted patterns can exhibit parallel behaviors and correlate recurrent deviations that have occurred in possibly different portions of the process, thus providing analysts with a valuable aid for investigating nonconforming behaviors. Our approach has been implemented as a plug-in of the ESub tool and evaluated using both synthetic and real-life logs.