A model for discovering correlations of ubiquitous things

Lina Yao, Quan Z. Sheng, Byron J. Gao, Anne H H Ngu, Xue Li

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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. Correlation discovery for ubiquitous things is critical for many important applications such as things search, recommendation, annotation, classification, clustering, composition, and management. In this paper, we propose a novel approach for discovering things correlation based on user, temporal, and spatial information captured from usage events of things. In particular, we use a spatio-temporal graph and a social graph to model things usage contextual information and user-thing relationships respectively. Then, we apply random walks with restart on these graphs to compute correlations among things. This correlation analysis lays a solid foundation and contributes to improved effectiveness in things management. To demonstrate the utility of our approach, we perform a systematic case study and comprehensive experiments on things annotation.

Original languageEnglish
Article number6729630
Pages (from-to)1253-1258
Number of pages6
JournalProceedings - IEEE International Conference on Data Mining, ICDM
Publication statusPublished - 2013
Externally publishedYes


  • correlation discovery
  • random walk with restart
  • Ubiquitous things


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