Unveiling correlations via mining human-thing interactions in the web of things

Lina Yao, Quan Z. Sheng, Anne H.H. Ngu, Xue Li, Boualem Benattalah

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

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.

Original languageEnglish
Article number62
Pages (from-to)1-25
Number of pages25
JournalACM Transactions on Intelligent Systems and Technology
Volume8
Issue number5
DOIs
Publication statusPublished - 1 Jun 2017

Keywords

  • Correlation discovery
  • Random walk with restart
  • Web of Things

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