{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T20:49:50Z","timestamp":1673124590482},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,4,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Detecting early enough the anomalous behavior of technical systems facilitates cost savings thanks to avoiding system\ndowntimes, guiding maintenance, or improving performance. The novel framework proposed in this paper processes event\nstreams originating from system monitoring for anomaly detection purposes. Therefore, statistical models characterizing\nthe normal behavior of the monitored system are learned from the events. Instead of having one coarse normal model for all\noperational states, the proposed framework contains a mechanism for automatically detecting different conditions of the\nsystem allowing for fine-tuned models for every condition. The performance of the framework is demonstrated by means of\na real-world application, where the log files of a large-scale printing machine are analyzed for anomalies.<\/jats:p>","DOI":"10.1515\/auto-2016-0070","type":"journal-article","created":{"date-parts":[[2017,4,13]],"date-time":"2017-04-13T10:00:53Z","timestamp":1492077653000},"page":"233-244","source":"Crossref","is-referenced-by-count":1,"title":["Conditional anomaly detection in event streams"],"prefix":"10.1515","volume":"65","author":[{"given":"Marco F.","family":"Huber","sequence":"first","affiliation":[{"name":"USU Software AG, R\u00fcppurrer Str. 1, 76137 Karlsruhe Germany"}]}],"member":"374","published-online":{"date-parts":[[2017,4,12]]},"container-title":["at - Automatisierungstechnik"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/auto.2017.65.issue-4\/auto-2016-0070\/auto-2016-0070.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2016-0070\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2016-0070\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T16:09:59Z","timestamp":1624291799000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2016-0070\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,12]]},"references-count":0,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,4,12]]},"published-print":{"date-parts":[[2017,4,29]]}},"alternative-id":["10.1515\/auto-2016-0070"],"URL":"https:\/\/doi.org\/10.1515\/auto-2016-0070","relation":{},"ISSN":["0178-2312","2196-677X"],"issn-type":[{"value":"0178-2312","type":"print"},{"value":"2196-677X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,4,12]]}}}