Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/83891
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Type: | Conference paper |
Title: | A model for discovering correlations of ubiquitous things |
Author: | Yao, L. Sheng, Q. Gao, B. Ngu, A. Li, X. |
Citation: | Proceedings of the 2013 IEEE 13th International Conference on Data Mining, ICDM 2013, 2013 / Hui Xiong, George Karypis, Bhavani Thuraisingham, Diane Cook, and Xindong Wu (Eds.): pp.1253-1258 |
Publisher: | IEEE Computer Society |
Publisher Place: | online |
Issue Date: | 2013 |
Series/Report no.: | IEEE International Conference on Data Mining |
ISSN: | 1550-4786 |
Conference Name: | International Conference on Data Mining (13th : 2013 : Dallas, Texas) |
Editor: | Xiong, H. Karypis, G. Thuraisingham, B. Cook, D. Wu, X. |
Statement of Responsibility: | Lina Yao, Quan Z. Sheng, Byron J. Gao, Anne H.H. Ngu, and Xue Li |
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. 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. |
Keywords: | ubiquitous things correlation discovery random walk with restart |
Rights: | © 2013 IEEE |
DOI: | 10.1109/ICDM.2013.87 |
Published version: | http://dx.doi.org/10.1109/icdm.2013.87 |
Appears in Collections: | Aurora harvest Computer Science publications |
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