On-The-Fly Processing of continuous high-dimensional data streams
作者: Raffaele VitaleAnna ZhyrovaJoão F. FortunaOnno E. de NoordAlberto FerrerHarald Martens
作者单位: 1Grupo de Ingeniería Estadística Multivariante, Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
2FFPW and CENAKVA, Institute of Complex Systems, University of South Bohemia in Ceske Budejovice, Zàmek 136, 37333 Novè Hrady, Czech Republic
3Department of Engineering Cybernetics, Faculty of Information Technology, Mathematics and Electrical Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
4Shell Global Solutions International B.V., Shell Technology Centre Amsterdam, PO Box 38000, 1030 BN Amsterdam, The Netherlands
5Idletechs AS, NTNU Innovation Centre, Richard Birkelansvei 2B, 7491 Trondheim, Norway
刊名: Chemometrics and Intelligent Laboratory Systems, 2017, Vol.161 , pp.118-129
来源数据库: Elsevier Journal
DOI: 10.1016/j.chemolab.2016.11.003
关键词: On-The-Fly Processing (OTFP)Bilinear modellingHigh-dimensional data streamsGeneralised Taylor expansionSingular Value Decomposition (SVD)BIG DATA analytics
原始语种摘要: Abstract(#br)A novel method and software system for rational handling of time series of multi-channel measurements is presented. This quantitative learning tool, the On-The-Fly Processing (OTFP), develops reduced-rank bilinear subspace models that summarise massive streams of multivariate responses, capturing the evolving covariation patterns among the many input variables over time and space. Thereby, a considerable data compression can be achieved without significant loss of useful systematic information.(#br)The underlying proprietary OTFP methodology is relatively fast and simple – it is linear/bilinear and does not require a lot of raw data or huge cross-correlation matrices to be kept in memory. Unlike conventional compression methods, the approach allows the high-dimensional data...
全文获取路径: Elsevier  (合作)
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