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Aurora: a data stream management system
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Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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Temporal management of RFID data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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Identifying RFID-embedded objects in pervasive healthcare applications
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Efficient RFID Data Imputation by Analyzing the Correlations of Monitored Objects
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
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Complex event processing model for distributed RFID network
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Real-time scalable attribute querying framework for RFID based smart spaces
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Intelligent Data Analysis
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To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications.