A model for discovering correlations of ubiquitous things

Yao, Lina, Sheng, Quan Z., Gao, Byron J., Ngu, Anne H. H. and Li, Xue (2013). A model for discovering correlations of ubiquitous things. In: Data Mining (ICDM), 2013 IEEE 13th International Conference on. 13th IEEE International Conference on Data Mining, ICDM 2013, Dallas, TX United States, (1253-1258). 7 - 10 December 2013. doi:10.1109/ICDM.2013.87

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Author Yao, Lina
Sheng, Quan Z.
Gao, Byron J.
Ngu, Anne H. H.
Li, Xue
Title of paper A model for discovering correlations of ubiquitous things
Conference name 13th IEEE International Conference on Data Mining, ICDM 2013
Conference location Dallas, TX United States
Conference dates 7 - 10 December 2013
Proceedings title Data Mining (ICDM), 2013 IEEE 13th International Conference on   Check publisher's open access policy
Journal name Proceedings - IEEE International Conference on Data Mining, ICDM   Check publisher's open access policy
Place of Publication Piscataway, NJ United States
Publisher I E E E
Publication Year 2013
Sub-type Fully published paper
DOI 10.1109/ICDM.2013.87
ISSN 1550-4786
Start page 1253
End page 1258
Total pages 6
Collection year 2014
Language eng
Abstract/Summary 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.
Subjects 2200 Engineering
Keyword Correlation discovery
Random walk with restart
Ubiquitous things
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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