Journal ArticleDOI
CAP: community activity prediction based on big data analysis
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TLDR
This article proposes an approach to extract community activity patterns by analyzing the big data collected from both the physical world and virtual social space and consists of community detection based on singular value decomposition and clustering, and community activity modeling based on tensors.Abstract:
Crowd sensing harnesses the power of the crowd by mobilizing a large number of users carrying various mobile and networked devices to collect data with the intrinsic multi-modal and large-volume features. With traditional methods, it is highly challenging to analyze the vast data volume generated by crowd sensing. In the era of big data, although several individual-oriented approaches are proposed to analyze human behavior based on big data, the common features of individual activity have not been fully investigated. In this article, we design a novel community- centric framework for community activity prediction based on big data analysis. Specifically, we propose an approach to extract community activity patterns by analyzing the big data collected from both the physical world and virtual social space. The proposed approach consists of community detection based on singular value decomposition and clustering, and community activity modeling based on tensors. The proposed approach is evaluated with a case study where a real dataset collected over a 15-month period is analyzed.read more
Citations
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References
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Pairwise interaction tensor factorization for personalized tag recommendation
TL;DR: The factorization model PITF (Pairwise Interaction Tensor Factorization) is presented which is a special case of the TD model with linear runtime both for learning and prediction and shows that this model outperforms TD largely in runtime and even can achieve better prediction quality.
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Big Data-Survey
P. S. G. Aruna Sri,M. Anusha +1 more
TL;DR: An observation on Hadoop architecture, different tools used for big data and its security issues, and how to reduce spot business patterns, anticipate diseases, conflict etc., is observed.
Book ChapterDOI
[8] Singular value decomposition: Application to analysis of experimental data
Eric R. Henry,James Hofrichter +1 more
TL;DR: The chapter describes the way in which the singular value decomposition of a noise-free data set for which the spectra, f, and concentration, c, vectors are known can be calculated from consideration of the integrated overlaps of these components.