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Floe: Designing A Continuous Data Flow Engine in consideration of Dynamic A
Austin College, US has reference to this Academic Journal, Floe: Designing A Continuous Data Flow Engine in consideration of Dynamic Applications on the CloudViktor Prasanna,Yogesh Simmhan, Alok Kumbhare, Sreedhar Natarajan 04/20/2012Workflow in addition to Stream Processing have been used so that in consideration of pipeline based applicationsD3 Science ? Dynamic, Distributed, Data Intensive ApplicationsDynamismData not being static in addition to flowing continuouslyData rates in addition to size being changing depending on domain requirements (QoS requirements)Workflows have compositional characteristics but limit dynamismStream Processing Systems provide real time processing but lack the compositional in addition to data diversity supportMap Reduce framework dynamism in data flow but severely lacks compositional flexibilityAn architecture which has the capability of providing Compositional capability in addition to allows real time stream processing Provide map reduce based key value exchangeMotivation2Data Flow Model Workflows follow Control Flow in addition to data flowFor continuous data, its difficult so that define strict control flowFloe follows a Data Flow ModelAllows in consideration of pipelined execution
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SVM-KNN Discriminative Nearest Neighbor Classification in consideration of Visual Category Recognition Multi-class Image Classification Caltech 101 Vanilla Approach KNN-SVM Features – Texture Features – Tangent Distance Comparison Features – Shape Context Features ? Geometric Blur Geometric Blur Geometric Blur KNN-SVN Results Learning Distance Metrics Frome, Singer, Malik Training Beyond Bags of Features: Spatial Pyramid Matching in consideration of Recognizing Natural Scene Categories Bags of Features alongside Pyramids Intersection of Histograms Features Results on scenes Results on Caltech 101 in addition to Graz Lessons Learned
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