Process mining (PM) is a body of techniques blending data science concepts with business process management (BPM). It utilizes event data recorded by IT systems that support process execution for a variety of tasks. These include the automated discovery of graphical process models, conformance checking between data and models, enhancement of process models with additional analytic information, run-time monitoring of processes and operational support. Thus far, research efforts in the PM area have primarily focused on algorithms and methods from a technical perspective. Less attention has been paid to supporting PM practitioners throughout the entire PM process. An important means for supporting human sense-making is through visualization of the analysis processes, the input (event data), and results. Appropriate visualizations can trigger hypotheses, drive additional analysis, reveal patterns, and raise insights.
Visual Analytics (VA) is a multidisciplinary approach, integrating aspects of data mining and knowledge discovery, information visualization, human-computer interaction, and cognitive science, with the aim to make complex phenomena more comprehensible, facilitate new insights, and enable knowledge discovery from data. VA leverages the specific strengths of computers and humans for the best possible outcome: on the one hand, computers are better at managing and processing large amounts of data by exploiting their computational power; on the other hand, humans have better perceptual and cognitive means, which enable them to visually perceive unexpected patterns and to interpret data.
VA and PM exhibit complementary traits that would greatly and mutually benefit from joining forces. However, the scientific body of literature reports on few endeavours towards this direction.
This special issue aims to contribute to the cross-fertilization of VA and PM, and welcomes works centred around methods and techniques stemming from, and contributing to, these disciplines.
Dr. Claudio Di Ciccio (Executive Guest Editor)Utrecht University, Utrecht, NetherlandsEmail: email@example.com Areas of Expertise: Process mining, automated reasoning in AI, blockchain technologies
Prof. Pnina SofferUniversity of Haifa, Haifa, IsraelEmail: firstname.lastname@example.org Areas of Expertise: Business process management, process mining, conceptual modeling
Prof. Barbara WeberUniversity of St. Gallen, St. Gallen, SwitzerlandEmail: email@example.com Areas of Expertise: Process modeling, process mining, adaptive software systems
Prof. Silvia MikschTU Wien, Vienna, AustriaEmail: firstname.lastname@example.org Areas of Expertise: Visualization and visual analytics over time and space, guidance, plan and process modeling