@article{a22617399c894171af5070260734022c,
title = "Domain-specific Event Abstraction",
abstract = "Process mining aims at deriving process knowledge from event logs, which contain data recorded during process executions. Typically, event logs need to be generated from process execution data, stored in different kinds of information systems. In complex domains like healthcare, data is available only at different levels of granularity. Event abstraction techniques allow the transformation of events to a common level of granularity, which enables effective process mining. Existing event abstraction techniques do not sufficiently take into account domain knowledge and, as a result, fail to deliver suitable event logs in complex application domains. This paper presents an event abstraction method based on domain ontologies. We show that the method introduced generates semantically meaningful high-level events, suitable for process mining; it is evaluated on real-world patient treatment data of a large U.S. health system.",
keywords = "Domain knowledge, Event abstraction, Healthcare, Process mining",
author = "Finn Klessascheck and Tom Lichtenstein and Martin Meier and Simon Remy and Sachs, {Jan Philipp} and Luise Pufahl and Riccardo Miotto and Erwin B{\"o}ttinger and Mathias Weske",
note = "Funding Information: Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award numbers S10OD026880. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. Funding Information: Acknowledgments. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award numbers S10OD026880. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. Publisher Copyright: {\textcopyright} Authors.; 24th International Conference on Business Information Systems, BIS 2021 ; Conference date: 15-06-2021 Through 17-06-2021",
year = "2021",
doi = "10.52825/bis.v1i.39",
language = "English",
volume = "1",
pages = "117--126",
journal = "Business Information Systems",
issn = "2747-9986",
}