Process mining and visual analytics are separate disciplines with the common goal of helping humans gain insight into and extract knowledge about relevant phenomena from complex data. Process mining (PM) is a rapidly growing discipline blending machine learning and data mining concepts with ideas taken from the field of business process management (BPM). It utilizes event data recorded by IT systems that support business process execution for a variety of tasks, from the automated discovery of graphical process models to operational support. Visual Analytics (VA) is a multidisciplinary approach that combines interactive, visual, and analytical methods to make complex phenomena more comprehensible, facilitate new insights, and enable knowledge discovery. VA research happens at the intersection of data mining and knowledge discovery, information visualization, human-computer interaction, and cognitive science.

Clearly, PM and VA are complementary research areas that would greatly and mutually benefit from joining forces. The combination of VA solutions with PM algorithms has the potential to render complex information structures more comprehensible and facilitate new insights. It also raises challenges and opportunities for analyzing process data in open-ended and under-specified exploratory analysis settings. Where PM aims to extract information and knowledge from event logs that often exhibit unexpected behavior and complex relationships, VA can provide mixed-initiative approaches in which humans and computers work together to extract valuable information from large and complex data, ultimately yielding crystallized, relevant insights.

So far, however, there have been very few interactions between the PM and VA communities. A first Dagstuhl seminar (23271, “Human in the (Process) Mines”) established a basis for scientific exchange and future collaboration. To further strengthen the identified synergies and identify novel promising directions, we propose a continuation seminar titled “Multi-faceted visual process mining and analytics” focusing on the challenges arising from the multi-faceted nature of processes, reflected in the multi-faceted data to be investigated. The relevant facets include time (when do processes happen), space (where do processes happen), topology (how are processes connected), object centricity (how are processes characterized), uncertainty (what are we unsure about), analytic provenance (how did we obtain our knowledge), and more.

This Dagstuhl Seminar will deal with challenges related to these different data facets, individually and in combination. As a general principle, we advocate that VA methods be an integral part of all phases of the PM process to facilitate a comprehensive multi-faceted data exploration, hypothesis generation, and presentation of results. The discussion will revolve around several aspects, including but not limited to: the data facets under analysis; the human factors at play; the catalog of aided tasks; visual, interactive, and computational methods; integration, scalability and evaluation fundamentals; mixed-initiative guidance; and general applicability of the devised solutions. These general seminar topics involve a variety of specific research questions. To name only a few: What kinds of multi-faceted VA methods can effectively support process sense-making? How can multi-faceting enhance human understandability of processes? How can we intertwine VA with PM to tackle quality, uncertainty, and provenance over time and space? How can VA help inject domain knowledge to reduce the process discovery search space?

Finally, this seminar aims to cross-fertilize and advance the fields of PM and VA, enrich future approaches to be developed, and 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, Pnina Soffer, Christian Tominski, and Katerina Vrotsou

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• Wolfgang Aigner (FH - St. Pölten, AT) [dblp]
• Gennady Andrienko (Fraunhofer IAIS - Sankt Augustin, DE) [dblp]
• Natalia V. Andrienko (Fraunhofer IAIS - Sankt Augustin, DE) [dblp]
• Iris Beerepoot (Utrecht University, NL) [dblp]
• Andrea Burattin (Technical University of Denmark - Lyngby, DK) [dblp]
• Lena Cibulski (Universität Rostock, DE)
• Claudio Di Ciccio (Utrecht University, NL) [dblp]
• Irit Hadar (University of Haifa, IL) [dblp]
• Marie-Christin Häge (Universität Mannheim, DE)
• Andreas Kerren (Linköping University, SE) [dblp]
• Philipp Koytek (Celonis Labs GmbH - München, DE) [dblp]
• Zhicheng Liu (University of Maryland - College Park, US) [dblp]
• Giovanni Meroni (Technical University of Denmark - Lyngby, DK) [dblp]
• Silvia Miksch (TU Wien, AT) [dblp]
• Manuel Resinas (University of Sevilla, ES) [dblp]
• Shazia Sadiq (University of Queensland - Brisbane, AU) [dblp]
• Hans-Jörg Schulz (Aarhus University, DK) [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]
• Maria-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]
• Peilin Yu (Linköping University, SE)
• Francesca Zerbato (TU Eindhoven, NL) [dblp]