Leveraging business process mining to obtain business intelligence and improve organizational performance

The utilization of process mining event logs has emerged as a pivotal strategy for organizations to achieve business intelligence, comprehend their processes, pinpoint inefficiencies, and assess performance. By enhancing process mining event logs with contextual data such as production and error reporting data, organizations can derive more profound insights into their operations, highlighting best practices, successful process paths, and areas for performance improvement. This dissertation explores the intrinsic value of analyzing event logs to acquire business intelligence and enhance organizational performance. Drawing on previous research in event log analysis, particularly in process discovery, this study aims to delve into untapped potentials within event logs, striving to extract comprehensive insights. Additionally, this research extends its scope to contribute to the domains of business intelligence and organizational behavior, focusing on organizational routines, routine performance, and error management. Through an exploratory journey comprising four papers, this study addresses the overarching research question by advancing the understanding of event logs in process mining and organizational behavior. The first paper introduces a framework for trace clustering, highlighting the substantial potential of event log analysis. The second paper proposes a process inefficiency index based on identifying unwanted patterns in process execution. The third paper offers a unified library of process measures, fostering further exploration of event logs. Finally, the fourth paper explores performance changes in response to errors in production processes, leveraging event log data to conduct a natural experiment. Through this multifaceted approach, this research enriches the process mining and organizational studies fields, contributing to understanding organizational routines and performance enhancement

Explaining clusterings of process instances

This paper presents a technique that aims to increase human understanding of trace clustering solutions. The clustering techniques under scrutiny stem from the process mining domain, where the clustering of process instances is deemed a useful technique to analyse process data with a large variety of behaviour. Until now, the most often used method to inspect clustering solutions in this domain is visual inspection of the clustering results. This paper proposes a more thorough approach based on the post hoc application of supervised learning with support vector machines on cluster results. Our approach learns concise rules to describe why a specific instance is included in a certain cluster based on specific control-flow based feature variables. An extensive experimental evaluation is presented showing that our technique outperforms alternatives. Likewise, we are able to identify features that lead to shorter and more accurate explanations.status: publishe