Queue - RFID
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Temporal management of RFID data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
An Introduction to RFID Technology
IEEE Pervasive Computing
A Pipelined Framework for Online Cleaning of Sensor Data Streams
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Towards correcting input data errors probabilistically using integrity constraints
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Adaptive cleaning for RFID data streams
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A deferred cleansing method for RFID data analytics
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Spatial correlation-based collaborative medium access control in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Bridging physical and virtual worlds: complex event processing for RFID data streams
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Complex event processing over unreliable RFID data streams
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Leveraging communication information among readers for RFID data cleaning
WAIM'11 Proceedings of the 12th international conference on Web-age information management
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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.