Correlation discovery in web of things

Lina Yao, Quan Z. Sheng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationWWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web
Place of PublicationNew York
PublisherACM Press
Number of pages2
ISBN (Print)9781450320382
Publication statusPublished - 2013
Externally publishedYes
Event22nd International Conference on World Wide Web, WWW 2013 - Rio de Janeiro, Brazil
Duration: 13 May 201317 May 2013


Other22nd International Conference on World Wide Web, WWW 2013
CityRio de Janeiro


  • correlation discovery
  • random walk with restart
  • Web of Things

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