Comparing Concept Drift Detection with Process Mining Software

Organisations have seen a rise in the volume of data corresponding to business processes being recorded. Handling process data is a meaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, business processes are subject to changes during their executions, adding complexity to their analysis. This paper aims at evaluating currently available process mining tools and software that handle concept drifts, i.e. changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these tools, comparing their differences, advantages, and disadvantages by testing against a log taken from a Process Control System. Thus, by highlighting the trade-off between the software, the paper gives the stakeholders the best options regarding their case use

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

Discovering Workflow Changes with Time-Based Trace Clustering

International audienceThis paper proposes a trace clustering approach to support process discovery of configurable, evolving process models. The clustering approach allows auditors to distinguish between different process variants within a timeframe, thereby visualizing the process evolution. The main insight to cluster entries is the “distance” between activities, i.e. the number of steps between an activity pair. By observing non-transient modifications on the distance, changes in the original process shape can be inferred and the entries clustered accordingly. The paper presents the corresponding algorithms and exemplifies its usage in a running example