Context: Process mining is an approach that exploits event logs to discover real processes executed in organizations, enabling them to (re)design and improve process models. Goal modelling, on the other hand, is a requirements engineering (RE) approach mainly used to analyze what-if situations and support decision making.
Problem: Common problems with process mining include the complexity of discovered “spaghetti” processes and a lack of goal-process alignment. Current process mining practices mainly focus on activities and do not benefit from considering stakeholder goals and requirements to manage complexity and alignment. The critical artifact that process mining practices rely on is the event log. However, using a raw version of real-life event logs will typically result in process models being too complex, unstructured, difficult to understand and, above all, not aligned with stakeholders’ goals.
Method: Involving goal-related factors can augment the precision and interpretability of mined models and help discover better opportunities to satisfy stakeholders. This thesis proposes three algorithms for goal-oriented process enhancement and discovery (GoPED) that show synergetic effects achievable by combining process mining and goal-oriented modelling. With GoPED, good historical experiences will be found within the event log to be used as a basis for inferring good process models, and bad experiences will be found to discover models to avoid. The goodness is defined in terms of alignment with regards to three categories of goal-related criteria:
• Case perspective: satisfaction of individual cases (e.g., patient, costumer) in terms of some goals;
• Goal perspective: overall satisfaction of some goals (e.g., to decrease waiting time) rather than individual cases; and
• Organization perspective: a comprehensive satisfaction level for all goals over all cases.
GoPED first adds goal-related attributes to conventional event characteristics (case identifier, activities, and timestamps), selects a subset of cases concerning goal-related criteria, and finally discovers a process model from that subset. For each criterion, an algorithm is developed to enable selecting the best subset of cases where the criterion holds. The resulting process models are expected to reproduce the desired level of satisfaction. The three GoPED algorithms were implemented in a Python tool. In addition, three other tools were implemented to complete a line of actions whose input is a raw event log and output is a subset of the event log selected with respect to the goal-related criteria. GoPED was used on real healthcare event logs (an illustrative example and a case study) to discover processes, and the performance of the tools was also assessed.
Results: The performance of the GoPED toolset for various sizes and configurations of event logs was assessed through extensive experiments. The results show that the three GoPED algorithms are practical and scalable for application to event logs with realistic sizes and types of configurations.
The GoPED method was also applied to the discovery of processes from the raw event log of the trajectories of patients with sepsis in a Dutch hospital, from their registration in the emergency room until their discharge. Although the raw data does not explicitly include goal-related information, some reasonable goals were derived from the data and a related research paper in consultation with a healthcare expert. The method was applied, and the resulting models were i) substantially simpler than the model dis-covered from the whole event log, ii) free from the drawbacks that using the whole event log causes, and iii) aligned with the predefined goals.
Conclusion: GoPED demonstrates the benefits of exploiting goal modelling capabilities to enhance event logs and select a subset of events to discover goal-aligned and simplified process models. The resulting process model can also be compared to a model discovered from the original event log to reveal new insights about the ability of different forms of process models to satisfy the stakeholders’ goals. Learning from good behaviours that satisfy goals and detecting bad behaviours that hurt them is an opportunity to redesign models, so they are simpler, better aligned with goals, and free from drawbacks that using the whole event log may cause.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/43084 |
| Date | 05 January 2022 |
| Creators | Ghasemi, Mahdi |
| Contributors | Amyot, Daniel |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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