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Furthermore, the rules are typically regarded as \u201ctrue by definition\u201d, disregarding the uncertainty introduced by natural process fluctuations. Against this background, we propose a novel, probability-based metric that provides interpretable metric values representing the probability that a record in a database is semantically consistent. Unlike existing approaches, our metric is not based on explicit rules, but on the equivalence between internal contradictions and anomalies within the data. Based on the isolation forest as a highly scalable anomaly detection method, it can handle high-dimensional big data. To account for the associated uncertainty, we combine the idea of the isolation forest with the Grubbs outlier test to assess the probability that a record is semantically consistent (i.e., not an anomaly). We demonstrate the practical applicability of our metric and evaluate its values using the case of production data of a German car manufacturer, as well as publicly available benchmark data for fault diagnosis. The evaluation shows that our metric can distinguish well between semantically consistent and inconsistent data.<\/jats:p>","DOI":"10.1145\/3732783","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T11:33:03Z","timestamp":1749036783000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Anomaly-based Assessment of Semantic Consistency: Design and Evaluation of a Novel Probability-based Metric in Cooperation with a German Car Manufacturer"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7109-0339","authenticated-orcid":false,"given":"Mathias","family":"Klier","sequence":"first","affiliation":[{"name":"Institute of Business Analytics, University of Ulm","place":["Ulm, Germany"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0048-8208","authenticated-orcid":false,"given":"Andreas","family":"Obermeier","sequence":"additional","affiliation":[{"name":"Institute of Business Analytics, University of Ulm","place":["Ulm, Germany"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9488-1401","authenticated-orcid":false,"given":"Christian","family":"Sparn","sequence":"additional","affiliation":[{"name":"Institute of Business Analytics, University of Ulm","place":["Ulm, Germany"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2601-4008","authenticated-orcid":false,"given":"Torben","family":"Widmann","sequence":"additional","affiliation":[{"name":"Institute of Business Analytics, University of Ulm","place":["Ulm, Germany"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.is.2014.07.006","article-title":"The rise of \u201cbig data\u201d on cloud computing: Review and open research issues","volume":"47","author":"Hashem Ibrahim A. 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