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- W2267524689 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." @default.
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- W2267524689 date "2015-12-24" @default.
- W2267524689 modified "2023-10-16" @default.
- W2267524689 title "Unveiling Contextual Similarity of Things via Mining Human-Thing Interactions in the Internet of Things" @default.
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- W2267524689 doi "https://doi.org/10.48550/arxiv.1512.08493" @default.
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