RFID Data Management and Analysis via Tensor Calculus

Traditional Radio-Frequency IDentication (RFID) applications have been focused on replacing bar codes in supply chain management. The importance of such new resource soared in recent years, mainly due to the retailers’ need of governing supply chains. However, due to the massive amount of RFID-related information in supply chain management, attaining satisfactory performances in analyzing such data sets is a challenging issue. Popular approaches provide hard-coded solutions, with high consumption of resources; moreover, these exhibit very inadequate adaptability when dealing with multidimensional queries, at various levels of granularity and complexity. In this paper we propose a novel model for supply chain management, aiming at generality, correctness, and simplicity. Such model is based on the first principles of multilinear algebra, specifically, of tensorial calculus. Leveraging our abstract algebraic framework, we envision a system allowing both quick decentralized on-line item discovery and centralized off-line massive business logic analysis, according to needs and requirements of supply chain actors. Being our computations based on vectorial calculus, we are able to exploit the underlying hardware processors, achieving a huge performance boost, as the experimental results show. Moreover, by storing only the needed data, and benefiting from linear properties, we are able to carry out the required computations even in high memory constrained environments, such as on mobile devices, and in parallel and distributed technologies by subdividing our tensor objects into sub-blocks, and processing them independently.