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The emerging applications in big data and social networks issue rapidly increasing demands on graph processing. Graph query operations that involve a large number of vertices and edges can be tremendously slow on traditional databases. The state-of-the-art graph processing systems and databases usually adopt master/slave architecture that potentially impairs their The contributions of this paper are...
Learning the underlying low-dimensional subspace from streaming incomplete high-dimensional observations data has attracted considerable attention lately. In this paper, we present a new computationally efficient Bayesian scheme for online low-rank subspace learning and matrix completion. The proposed scheme builds upon a properly defined hierarchical Bayesian model that explicitly imposes low rank...
The trending evolution towards the Internet of things and the general increase in broadband are constantly creating large volumes of data that need to be processed to extract further knowledge. Recently, the cloud computing model has seen the evolution from the initial scenario of a public cloud offering its resources to customers through virtualization and Internet, toward the concept of hybrid cloud,...
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