{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T16:10:27Z","timestamp":1739981427601,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>In the context of time-critical applications there exists the need of clustering data streams so as to provide approximated solutions in the shortest possible time, in order to capture in real-time the evolution of physical or social phenomena. In this work, a nature-inspired algorithm for clustering of evolving big data stream is presented, which is designed to be executed on many-core GPU architectures.<\/jats:p>","DOI":"10.3233\/978-1-61499-843-3-317","type":"book-chapter","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T15:30:51Z","timestamp":1739979051000},"source":"Crossref","is-referenced-by-count":0,"title":["A Nature-Inspired, Anytime and Parallel Algorithm for Data Stream Clustering"],"prefix":"10.3233","author":[{"family":"Spezzano Giandomenico","sequence":"additional","affiliation":[]},{"family":"Vinci Andrea","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Advances in Parallel Computing","Parallel Computing is Everywhere"],"original-title":[],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T15:53:10Z","timestamp":1739980390000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-842-6&spage=317&doi=10.3233\/978-1-61499-843-3-317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-843-3-317","relation":{},"ISSN":["0927-5452"],"issn-type":[{"value":"0927-5452","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}