A Framework for Outlier Mining in RFID data

Radio frequency identification (RFID) applications are emerging as key components in object tracking and supply chain management systems. In next future almost every major retailer will use RFID systems to track the shipment of products from suppliers to warehouses. Due to RFID readings features this will result in a huge amount of information generated by such systems when costs will be at a level such that each individual item could be tagged thus leaving a trail of data as it moves through different locations. We define a technique for efficiently detecting anomalous data in order to prevent problems related to inefficient shipment or fraudulent actions. Since items usually move together in large groups through distribution centers and only in stores do they move in smaller groups we exploit such a feature in order to design our technique. The preliminary experiments show the effectiveness of our approach.