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. Over the years, a large body of research has emerged, and the growing maturity of this discipline has led to an increasing adoption in industry, with a wealth of available tools, and thousands of organizations applying PM.

So 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. Although the Process Mining Manifesto raised the need to improve the usability of PM already a decade ago, this challenge has been tackled only in part. 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. Currently, the vast majority of PM approaches are tied to visual process representations, which are typically anchored in known process modeling notations (e.g., Petri nets, BPMN). As a result, the control-flow of the process (namely, its sequence of activities) is the main view, whereas additional perspectives (e.g., time, resources, rules) are less emphasized in the resulting visualizations.

In contrast, Visual Analytics (VA) applies to any kind of data. Defined as "the science of analytical reasoning facilitated by interactive visual interfaces", VA is a multidisciplinary approach, integrating aspects of data mining and knowledge discovery, information visualization, human-computer interaction, and cognitive science. 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. As opposed to PM approaches, VA methods are not guided by models whose semantics are well defined. Incorporating such models can form an opportunity for VA.

While these two research disciplines face similar challenges in different contexts, so far there have been few interactions and cross-fertilization efforts between the respective communities. This Dagstuhl Seminar intends to bring together researchers from both communities and foster joint research efforts and collaborations, to advance both fields and enrich future approaches to be developed.

The seminar will deal with topics that are at the intersection of PM and VA, and can potentially contribute to both areas. Discussion topics include, but are not limited to: How can VA methods effectively support sense-making for various PM goals? How can intertwined VA and PM tackle quality and uncertainty over time and space, explore process models and compare them, and utilize principles of guidance and knowledge-assisted VA? Which kinds of visualization and interaction techniques are needed to properly support PM? How can VA solutions scale from single event sequences to multiple event logs? How can cognitive processes involved in PM (e.g., generation of hypotheses) be supported using VA concepts? How to guide the human analyst in determining what kinds of analyses to perform? As a specific outcome, the research challenges at the intersection of PM and VA are intended to be mapped and a research agenda should be derived to exploit the potential for cross-fertilization. As such, the seminar aims to serve as an incubator for sustained collaborations leading to joint scientific efforts and initiatives to attract research funding.

Creative Commons BY 4.0

Claudio Di Ciccio, Silvia Miksch, Pnina Soffer, and Barbara Weber

• Wolfgang Aigner (FH - St. Pölten , AT) [dblp]
• Anti Alman (University of Tartu, EE) [dblp]
• Alessio Arleo (TU Wien, AT) [dblp]
• Iris Beerepoot (Utrecht University, NL) [dblp]
• Axel Buehler (Olympus - Hamburg, DE)
• Andrea Burattin (Technical University of Denmark - Lyngby, DK) [dblp]
• Claudio Di Ciccio (Sapienza University of Rome, IT) [dblp]
• Chiara Di Francescomarino (University of Trento, IT) [dblp]
• Irit Hadar (University of Haifa, IL) [dblp]
• Mieke Jans (Hasselt University, BE) [dblp]
• Philipp Koytek (Celonis Labs GmbH - München, DE) [dblp]
• Monika Malinova Mandelburger (Wirtschaftsuniversität Wien, AT) [dblp]
• Andrea Marrella (Sapienza University of Rome, IT) [dblp]
• Jan Mendling (HU Berlin, DE) [dblp]
• Giovanni Meroni (Technical University of Denmark - Lyngby, DK) [dblp]
• Silvia Miksch (TU Wien, AT) [dblp]
• Luise Pufahl (TU München - Heilbronn, DE) [dblp]
• Jana-Rebecca Rehse (Universität Mannheim, DE) [dblp]
• Manuel Resinas (University of Sevilla, ES) [dblp]
• Shazia Sadiq (University of Queensland - Brisbane, AU) [dblp]
• Daniel Schuster (Fraunhofer FIT - Sankt Augustin, DE) [dblp]
• Pnina Soffer (University of Haifa, IL) [dblp]
• Christian Tominski (Universität Rostock, DE) [dblp]
• Cagatay Turkay (University of Warwick - Coventry, GB) [dblp]
• Stef Van den Elzen (TU Eindhoven, NL) [dblp]
• Sebastiaan Johannes van Zelst (Celonis Labs GmbH - München, DE) [dblp]
• Mari-Cruz Villa-Uriol (University of Sheffield, GB) [dblp]
• Tatiana von Landesberger (Universität Köln, DE) [dblp]
• Katerina Vrotsou (Linköping University, SE) [dblp]
• Barbara Weber (Universität St. Gallen, CH) [dblp]
• Francesca Zerbato (Universität St. Gallen, CH) [dblp]
• Lisa Zimmermann (Universität St. Gallen, CH) [dblp]