Abstract
Radio frequency identification (RFID) applications set to play an essential role in object tracking and supply chain management systems. In future RFID technologies are expected that every major retailer will use RFID systems to track the movement of products from suppliers to warehouses, transportation and distribution. The volume of information generated by such systems is enormous. Despite all these, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information and very high volumes. In this paper we propose a novel data interpretation and compression substrate over RFID streams to address these challenges in enterprise supply chain environments. Our results demonstrate that our inference techniques provide good accuracy while retaining efficiency, and our compression algorithm yields significant reduction in data volume.
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