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With recent advances in radio-frequency identification (RFID), wirelesssensor networks, and Web services, physical things are becoming an integralpart of the emerging ubiquitous Web. Finding correlations of ubiquitous thingsis a crucial prerequisite for many important applications such as thingssearch, discovery, classification, recommendation, and composition. Thisarticle presents DisCor-T, a novel graph-based method for discoveringunderlying connections of things via mining the rich content embodied inhuman-thing interactions in terms of user, temporal and spatial information. Wemodel these various information using two graphs, namely spatio-temporal graphand social graph. Then, random walk with restart (RWR) is applied to findproximities among things, and a relational graph of things (RGT) indicatingimplicit correlations of things is learned. The correlation analysis lays asolid foundation contributing to improved effectiveness in things management.To demonstrate the utility, we develop a flexible feature-based classificationframework on top of RGT and perform a systematic case study. Our evaluationexhibits the strength and feasibility of the proposed approach.

Unveiling Contextual Similarity of Things via Mining Human-Thing Interactions in the Internet of Things