Lina Yao
Quan Z. Sheng
Anne H. H. Ngu
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 among 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 approach for discovering underlying connections of things via mining the rich content embodied in the human-thing interactions in terms of user, temporal, and spatial information. We model this various information using two graphs, namely a spatio-temporal graph and a 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 and analytics. To demonstrate the utility of the proposed approach, 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.
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Index Terms
- Unveiling Correlations via Mining Human-Thing Interactions in the Web of Things
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Varadraj Prabhu Gurupur
Today we find ourselves in a situation where there is an overabundance of data in information networks. This being the case, there is an overwhelming need to identify pieces of data that could be critical in decision making. Time and time again, graph theory has played a critical role in facilitating this decision making. Here, the semantics of data are considered pivotal when compared to the content. In this paper, the authors illustrate a unique method for identifying correlations between data elements contained on the Internet. The uniqueness of their approach lies in the fact that they use social and spatiotemporal graphs to perform the required analysis. The authors use a real-life situation to illustrate their technique, along with the associated algorithm and mathematics. The real-life situation considered here is that of various electronic items located in the kitchen and their usage. The items are tracked using radio-frequency identification (RFID)-based mobility detection. This is a very interesting research topic and the associated experiment is well illustrated. The application of this presented method may be multi-fold and can further the science described in the paper. Online Computing Reviews Service
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