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Multi-Faceted Process Visual Analytics

Authors: Van den Elzen, Stef; JANS, Mieke; Tominski, Christian; van Zelst, Sebastiaan Johannes; Villa-Uriol, Mari-Cruz;

Multi-Faceted Process Visual Analytics

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 23271, “Human in the (process) mines”. The seminar dealt with topics that are at the intersection of process mining and visual analytics, and can potentially contribute to both areas. Process mining is a discipline blending data science concepts with business process management. It utilizes event data recorded by IT systems for a variety of tasks, including 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. Ultimately, the purpose of process mining is to make sense of event data and answer business and domain-related questions to support domain-specific goals. Visual Analytics, defined as “the science of analytical reasoning facilitated by interactive visual interfaces,” is a multidisciplinary approach, integrating aspects of data mining and knowledge discovery, information visualization, human-computer interaction, and cognitive science to support humans in making sense of various kinds of data. While these two research disciplines face similar challenges in different contexts, there have been few interactions and cross-fertilization efforts between the respective communities so far. This Dagstuhl Seminar is intended 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.

Keywords

Human-centered computing → Visual analytics, process mining, visual analytics, Applied computing → Business process management, human in the loop

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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