Abstract
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.
This work has been partially funded by the Italian Ministry of Research, grant number RBIP06BZW8, FIRB project “Advanced tracking system in intermodal freight transportation”.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Smith, A.: Exploring radio frequency identification technology and its impact on business systems. Information Management and Computer Security 13(1), 16–28 (2005)
Tajima, M.: Strategic value of RFID in supply chain management. Journal of Purchasing & Supply Management (2007)
EPC Global, http://www.epcglobalinc.org/home
Angeles, R.: RFID technologies: supply-chain applications and implementation issues. Information Systems Management 22(1), 51–65 (2005)
Bai, Y., Wang, F., Liu, P., Zaniolo, C., Liu, S.: RFID data processing with a data stream query language. In: Proc. of the 23rd International Conference on Data Engineering, ICDE 2007 (2007)
Gonzalez, H., Han, J., Li, X., Klabjan, D.: Warehousing and analyzing massive RFID data sets. In: Proc. of the 22nd Int. Conf. on Data Engineering, ICDE, p. 83 (2006)
Lee, C.H., Chung, C.W.: Efficient storage scheme and query processing for supply chain management using RFID. In: Proc. of the Int. Conf. on Management of Data, SIGMOD, pp. 291–302 (2008)
Papadimitriou, S., Sun, J., Faloutsos, C.: Streaming pattern discovery in multiple time-series. In: Proc. of the 31st Int. Conf. on Very Large Data Bases, VLDB, pp. 697–708 (2005)
Bai, Y., Wang, F., Liu, P., Zaniolo, C., Liu, S.: RFID data processing with a data stream query language. In: Proc. of Int. Conf. on Data Engineering, ICDE, pp. 1184–1193 (2007)
Jeffery, S.R., Garofalakis, M.N., Franklin, M.J.: Adaptive cleaning for RFID data streams. In: Proc. of the 32nd Int. Conf. on Very Large Data Bases, VLDB, pp. 163–174 (2006)
Jeffery, S.R., Alonso, G., Franklin, M.J., Hong, W., Widom, J.: A pipelined framework for online cleaning of sensor data streams. In: Proc. of the 22nd Int. Conf. on Data Engineering, ICDE, p. 140 (2006)
Wang, F., Liu, S., Liu, P., Bai, Y.: Bridging Physical and Virtual Worlds: Complex Event Processing for RFID Data Streams. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 588–607. Springer, Heidelberg (2006)
Hoffman, M., Kunze, R.: Linear Algebra. Prentice Hall (1971)
Abraham, R., Marsden, J.E., Ratiu, T.: Manifolds, Tensor Analysis, and Applications. Springer (1988)
Heinbockel, J.H.: Introduction to Tensor Calculus and Continuum Mechanics. Trafford Publishing (2001)
Jancewicz, B.: The extended grassmann algebra of ℝ3. In: Clifford (Geometric) Algebras with Applications to Physics, Mathematics, and Engineering. Birkhäuser, Boston (1996)
Derakhshan, R., Orlowska, M.E., Li, X.: RFID data management: Challenges and opportunities. In: Proc. of the IEEE Int. Conf. on RFID, pp. 175–182 (2007)
Bondy, A., Murty, U.S.R.: Graph Theory. Graduate Texts in Mathematics. Springer (2010)
Chartrand, G.: Introductory Graph Theory. Dover Publications (1984)
Duff, I.S., Erisman, A.M., Reid, J.K.: Direct Methods for Sparse Matrices. Numerical Mathematics and Scientific Computation. Oxford Univ. Press (1989)
Davis, T.A.: Direct Methods for Sparse Linear Systems. SIAM (2006)
Osterby, O., Zlatev, Z.: Direct Methods for Sparse Matrices. LNCS, vol. 157. Springer, Heidelberg (1983)
Sears, M.P., Bader, B.W., Kolda, T.G.: Parallel implementation of tensor decompositions for large data analysis. In: SIAM AN 2009. SIAM (July 2009)
Lin, C.Y., Liu, J.S., Chung, Y.C.: Efficient representation scheme for multidimensional array operations. IEEE Transactions on Computers 51, 327–345 (2002)
Lin, C.Y., Chung, Y.C., Liu, J.S.: Efficient data compression methods for multidimensional sparse array operations based on the ekmr scheme. IEEE Trans. Comput. 52, 1640–1646 (2003)
Bader, B.W., Kolda, T.G.: Algorithm 862: Matlab tensor classes for fast algorithm prototyping. ACM Transactions on Mathematical Software 32(4), 635–653 (2006)
Bader, B.W., Kolda, T.G.: Efficient MATLAB computations with sparse and factored tensors. SIAM Journal on Scientific Computing 30(1), 205–231 (2007)
Bertsekas, D.P., Tsitsiklis, J.N.: Parallel and Distributed Computation: Numerical Methods. Athena Scientific (1997)
Williams, S., Oliker, L., Vuduc, R., Shalf, J., Yelick, K., Demmel, J.: Optimization of sparse matrix-vector multiplication on emerging multicore platforms. In: Proc. SC 2007: High Performance Computing, Networking, and Storage Conference, pp. 10–16 (2007)
Johnson, R.W., Huang, C.H., Johnson, J.R.: Multilinear algebra and parallel programming. In: Proceedings of the 1990 ACM/IEEE Conference on Supercomputing, Supercomputing 1990, pp. 20–31. IEEE Computer Society Press, Los Alamitos (1990)
Buluç, A., Fineman, J.T., Frigo, M., Gilbert, J.R., Leiserson, C.E.: Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks. In: Proc. of the 21st Annual Symposium on Parallelism in Algorithms and Architectures, SPAA 2009, pp. 233–244. ACM (2009)
Iverson, K.: A programming language. In: Proc. of the AFIPS Spring Joint Computer Conference (1962)
Kleene, S.C.: Introduction to Metamathematics. Van Nostrand Rheinhold (1952)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
De Virgilio, R., Milicchio, F. (2012). RFID Data Management and Analysis via Tensor Calculus. In: Hameurlain, A., Küng, J., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems VII. Lecture Notes in Computer Science, vol 7720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35332-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-35332-1_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35331-4
Online ISBN: 978-3-642-35332-1
eBook Packages: Computer ScienceComputer Science (R0)