Correlation discovery in web of things

With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, Web of Things (WoT) is gaining a considerable momentum as an emerging paradigm where billions of physical objects will be interconnected and present over the World Wide Web. One inevitable challenge in the new era of WoT lies in how to efficiently and effectively manage things, which is critical for a number of important applications such as object search, recommendation, and composition. In this paper, we propose a novel approach to discover the correlations of things by constructing a relational network of things (RNT) where similar things are linked via virtual edges according to their latent correlations by mining three dimensional information in the things usage events in terms of user, temporality and spatiality. With RNT, many problems centered around things management such as objects classification, discovery and recommendation can be solved by exploiting graph-based algorithms. We conducted experiments using real-world data collected over a period of four months to verify and evaluate our model and the results demonstrate the feasibility of our approach.