@article{574,
  keywords = {Visual analytics, Visual Process Mining},
  author = {Claudio Di Ciccio and Silvia Miksch and Pnina Soffer and Barbara Weber and Giovanni Meroni},
  title = {Human in the (Process) Mines (Dagstuhl Seminar 23271)},
  abstract = {<p>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.</p>},
  year = {2024},
  journal = {Dagstuhl Reports},
  volume = {13},
  number = {7},
  pages = {1-33},
  publisher = {Schloss Dagstuhl &mdash; Leibniz-Zentrum für Informatik},
  address = {Dagstuhl, Germany},
  issn = {2192-5283},
  url = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.7.1},
  doi = {10.4230/DagRep.13.7.1},
}