{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T23:09:03Z","timestamp":1719788943167},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,29]]},"abstract":"<jats:p>The process of mainframe machines managing and administration requires not only specialized expert knowledge based on many years of experience but also on appropriate tools provided by a machine performance management system, e.g. the Resource Measurement Facility (RMF). The aim of this paper is to show some preliminary results of Z-RAYS system construction that is built basing on machine learning (ML) techniques. It allows automatic detection of anomalies and generation of early warnings about some errors that can appear in the mainframe to support mainframe management process. Presented results are based on extensive simulations that were done basing on the IBM emulator. We focus on determining the degree of the metrics variability, the degree of the data repeatability in metrics, some approaches in metrics anomaly detection and solutions for event correlation detection in metrics.<\/jats:p>","DOI":"10.3233\/faia210236","type":"book-chapter","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T17:33:45Z","timestamp":1635874425000},"source":"Crossref","is-referenced-by-count":1,"title":["The Support System for Anomaly Detection with Application in Mainframe Management Process"],"prefix":"10.3233","author":[{"given":"Dominik","family":"Strza\u0142ka","sequence":"first","affiliation":[{"name":"Department of Complex Systems, Rzesz\u00f3w University of Technology, Al. Powsta\u0144c\u00f3w Warszawy 12, 35-959 Rzesz\u00f3w, Poland"}]},{"given":"Alicja","family":"Gerka","sequence":"additional","affiliation":[{"name":"Department of Complex Systems, Rzesz\u00f3w University of Technology, Al. Powsta\u0144c\u00f3w Warszawy 12, 35-959 Rzesz\u00f3w, Poland"}]},{"given":"Bartosz","family":"Kowal","sequence":"additional","affiliation":[{"name":"Department of Complex Systems, Rzesz\u00f3w University of Technology, Al. Powsta\u0144c\u00f3w Warszawy 12, 35-959 Rzesz\u00f3w, Poland"}]},{"given":"Pawe\u0142","family":"Kura\u015b","sequence":"additional","affiliation":[{"name":"Department of Complex Systems, Rzesz\u00f3w University of Technology, Al. Powsta\u0144c\u00f3w Warszawy 12, 35-959 Rzesz\u00f3w, Poland"}]},{"given":"Grzegorz","family":"Leopold","sequence":"additional","affiliation":[{"name":"Z-RAYS, Plac Andersa 7, 61-894 Pozna\u0144, Poland"}]},{"given":"Micha\u0142","family":"Lewicz","sequence":"additional","affiliation":[{"name":"Z-RAYS, Plac Andersa 7, 61-894 Pozna\u0144, Poland"}]},{"given":"Dawid","family":"Jaworski","sequence":"additional","affiliation":[{"name":"Department of Mathematical Modelling, Rzesz\u00f3w University of Technology, Al. Powsta\u0144c\u00f3w Warszawy 12, 35-959 Rzesz\u00f3w, Poland"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Modern Management based on Big Data II and Machine Learning and Intelligent Systems III"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA210236","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T17:33:47Z","timestamp":1635874427000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210236","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,29]]}}}