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dc.creatorGeorgakopoulos S.V., Tasoulis S.K., Plagianakos V.P.en
dc.date.accessioned2023-01-31T07:40:22Z
dc.date.available2023-01-31T07:40:22Z
dc.date.issued2015
dc.identifier10.1109/BigData.2015.7364010
dc.identifier.isbn9781479999255
dc.identifier.urihttp://hdl.handle.net/11615/72071
dc.description.abstractThe recent technological advancements in cloud computing and the access in increasing computational power has led in undertaking the data processing derived by mobile devices. In particular, when these data are high dimensional this is indispensable, since the mobile device has to balance its processing functionalities to additional services. However, developing efficient algorithms could allow various types of analysis to be performed locally, avoiding the necessity of a constantly connected device. In this work, we present a methodology that combines lightweight dimensionality reduction and change detection techniques. The experimental results justify its impressive performance and subsequently its usefulness in several tasks. © 2015 IEEE.en
dc.language.isoenen
dc.sourceProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84963744687&doi=10.1109%2fBigData.2015.7364010&partnerID=40&md5=4950b3e39ca57d605333141083286a58
dc.subjectAlgorithmsen
dc.subjectClustering algorithmsen
dc.subjectData communication systemsen
dc.subjectData handlingen
dc.subjectData miningen
dc.subjectMobile devicesen
dc.subjectPrincipal component analysisen
dc.subjectSignal detectionen
dc.subjectCumulative sumsen
dc.subjectData streamen
dc.subjectDimensionality reductionen
dc.subjectHigh dimensional dataen
dc.subjectHigh-dimensional data streamsen
dc.subjectIncremental principal component analysisen
dc.subjectProcessing functionalityen
dc.subjectTechnological advancementen
dc.subjectBig dataen
dc.subjectInstitute of Electrical and Electronics Engineers Inc.en
dc.titleEfficient change detection for high dimensional data streamsen
dc.typeconferenceItemen


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