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Despite its promise, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information, and very high volumes. In this paper, the authors present a novel data interpretation and compression substrate over RFID streams to address these challenges. The substrate employs a time-varying graph model to efficiently capture possible object locations and inter-object relationships such as containment from raw RFID streams. It then employs a probabilistic algorithm to estimate the most likely location and containment for each object. By performing such online interpretation, it enables online compression that recognizes and removes redundant information from the output stream of this substrate.
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