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26 April 2024 |
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Article overview
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Unveiling Contextual Similarity of Things via Mining Human-Thing Interactions in the Internet of Things | | Lina Yao
; Quan Z. Sheng
; Anne H.H. Ngu
; Byron J. Gao
; Boualem Benatallah
; Xue Li
; | | Date: |
24 Dec 2015 | | Abstract: | With recent advances in radio-frequency identification (RFID), wireless
sensor networks, and Web services, physical things are becoming an integral
part of the emerging ubiquitous Web. Finding correlations of ubiquitous things
is a crucial prerequisite for many important applications such as things
search, discovery, classification, recommendation, and composition. This
article presents DisCor-T, a novel graph-based method for discovering
underlying connections of things via mining the rich content embodied in
human-thing interactions in terms of user, temporal and spatial information. We
model these various information using two graphs, namely spatio-temporal graph
and social graph. Then, random walk with restart (RWR) is applied to find
proximities among things, and a relational graph of things (RGT) indicating
implicit correlations of things is learned. The correlation analysis lays a
solid foundation contributing to improved effectiveness in things management.
To demonstrate the utility, we develop a flexible feature-based classification
framework on top of RGT and perform a systematic case study. Our evaluation
exhibits the strength and feasibility of the proposed approach. | | Source: | arXiv, 1512.8493 | | Services: | Forum | Review | PDF | Favorites | |
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