Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-00887-0_15
Title: Efficient RFID data imputation by analyzing the correlations of monitored objects
Authors: Gu, Y.
Yu, G.
Chen, Y. 
Ooi, B.C. 
Issue Date: 2009
Source: Gu, Y.,Yu, G.,Chen, Y.,Ooi, B.C. (2009). Efficient RFID data imputation by analyzing the correlations of monitored objects. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5463 : 186-200. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-00887-0_15
Abstract: As a promising technology for tracing the product and human flows, Radio Frequency Identification (RFID) has received much attention within database community. However, the problem of missing readings restricts the application of RFID. Some RFID data cleaning algorithms have therefore been proposed to address this problem. Nevertheless, most of them fill up missing readings simply based on the historical readings of independent monitored objects. While, the correlations(spatio-temporal closeness) among the monitored objects are ignored.We observe that the spatio-temporal correlations of monitored objects are very useful for imputing the missing RFID readings. In this paper,we propose a data imputation model for RFID by efficiently maintaining and analyzing the correlations of the monitored objects. Optimized data structures and imputation strategies are developed. Extensive simulated experiments have demonstrated the effectiveness of the proposed algorithms.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/42005
ISBN: 9783642008863
ISSN: 03029743
DOI: 10.1007/978-3-642-00887-0_15
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