Visual Analytics in Process Mining for Supporting Business Process Improvement Antonia KAOUNI a , Georgia THEODOROPOULOU a,1 , Alexandros BOUSDEKIS a , Athanasios VOULODIMOS a , and Georgios MIAOULIS a a Department of Informatics and Computer Engineering, University of West Attica, Athens, Greece Abstract. The increasing amounts of data have affected conceptual modeling as a research field. In this context, process mining involves a set of techniques aimed at extracting a process schema from an event log generated during process execution. While automatic algorithms for process mining and analysis are needed to filter out irrelevant data and to produce preliminary results, visual inspection, domain knowledge, human judgment and creativity are needed for proper interpretation of the results. Moreover, a process discovery on an event log usually results in complicated process models not easily comprehensible by the business user. To this end, visual analytics has the potential to enhance process mining towards the direction of explainability, interpretability and trustworthiness in order to better support human decisions. In this paper we propose an approach for identifying bottlenecks in business processes by analyzing event logs and visualizing the results. In this way, we exploit visual analytics in the process mining context in order to provide explainable and interpretable analytics results for business processes without exposing to the user complex process models that are not easily comprehensible. The proposed approach was applied to a manufacturing business process and the results show that visual analytics in the context of process mining is capable of identifying bottlenecks and other performance-related issues and exposing them to the business user in an intuitive and non-intrusive way. Keywords. Visual analytics, process analytics, process intelligence, visualization, data analytics, business process management 1. Introduction The amount of data recorded in various domains has been growing exponentially [1]. This offers opportunities for algorithmic techniques, but also creates new challenges. The availability of data has extended conceptual modeling as a research field of manually created models with automatic techniques for generating models from data [2]. Process mining is one of these recent extensions. Process mining involves a set of techniques aimed at extracting a process schema from an event log generated during process execution [3][4] Combining automatic analysis of event logs and visualization methods face several challenges [3]. While automatic algorithms for process mining and analysis are certainly needed to filter out irrelevant data and to produce preliminary results, visual inspection, domain knowledge, human judgment and creativity are needed for proper 1 Corresponding Author, Georgia Theodoropoulou, University of West Attica, 12243 Ag. Spyridonos Athens, Greece; E-mail: gtheodoropoulou@uniwa.gr. Novelties in Intelligent Digital Systems C. Frasson et al. (Eds.) © 2021 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/FAIA210089 166