{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T13:50:56Z","timestamp":1719150656065},"reference-count":0,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>When microservices-based architectures are adopted for an enterprise application, a basic requirement would be an evaluation of the performance with the objective of continuous monitoring and improved efficiency. This evaluation helps businesses obtain a quantitative measure of the benefits of a shift from monolith to microservices. Additionally, the metrics obtained could be used as a mechanism for continuous improvement of production application. This research proposes a model based on the principles of data mining called stream analytics feedback and optimization (SAFAO), which can be used to achieve a continuous optimization of microservices. Stream analytics is due to the fact that the analysis is performed on online application with continuously generated lived data. This approach has been tested in a simulated production environment based on Docker containers. The authors were able to establish empirical measures which were continuously extracted via a data mining methodology and then fed back into the running application through configuration management. The results show a continuous improvement in the performance of the microservices as indicated in the results presented in this research.<\/jats:p>","DOI":"10.4018\/ijeis.2021010102","type":"journal-article","created":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T15:18:20Z","timestamp":1607354300000},"page":"22-43","source":"Crossref","is-referenced-by-count":4,"title":["Microservices Data Mining for Analytics Feedback and Optimization"],"prefix":"10.4018","volume":"17","author":[{"given":"Kindson","family":"Munonye","sequence":"first","affiliation":[{"name":"Budapest University of Technology and Economics, Hungary"}]},{"given":"P\u00e9ter","family":"Martinek","sequence":"additional","affiliation":[{"name":"Budapest University of Technology and Economics, Hungary"}]}],"member":"2432","container-title":["International Journal of Enterprise Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=268361","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T13:53:20Z","timestamp":1651845200000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJEIS.2021010102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":0,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.4018\/ijeis.2021010102","relation":{},"ISSN":["1548-1115","1548-1123"],"issn-type":[{"value":"1548-1115","type":"print"},{"value":"1548-1123","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]}}}