The Community for Technology Leaders
Green Image
Issue No. 01 - January (2010 vol. 22)
ISSN: 1041-4347
pp: 90-104
Xiaolei Li , Microsoft AdCenter Labs 1, Redmond
Hong Cheng , University of Illinois at Urbana-Champaign, Urbana
Diego Klabjan , Northwestern University. Evanston
Tianyi Wu , University of Illinois at Urbana-Champaign, Urbana
Hector Gonzalez , Google, Inc., Mountain View
Jiawei Han , University of Illinois at Urbana-Champaign, Urbana
ABSTRACT
Massive Radio Frequency Identification (RFID) data sets are expected to become commonplace in supply chain management systems. Warehousing and mining this data is an essential problem with great potential benefits for inventory management, object tracking, and product procurement processes. Since RFID tags can be used to identify each individual item, enormous amounts of location-tracking data are generated. With such data, object movements can be modeled by movement graphs, where nodes correspond to locations and edges record the history of item transitions between locations. In this study, we develop a movement graph model as a compact representation of RFID data sets. Since spatiotemporal as well as item information can be associated with the objects in such a model, the movement graph can be huge, complex, and multidimensional in nature. We show that such a graph can be better organized around gateway nodes, which serve as bridges connecting different regions of the movement graph. A graph-based object movement cube can be constructed by merging and collapsing nodes and edges according to an application-oriented topological structure. Moreover, we propose an efficient cubing algorithm that performs simultaneous aggregation of both spatiotemporal and item dimensions on a partitioned movement graph, guided by such a topological structure.
INDEX TERMS
RFID, data warehousing, data models.
CITATION
Xiaolei Li, Hong Cheng, Diego Klabjan, Tianyi Wu, Hector Gonzalez, Jiawei Han, "Modeling Massive RFID Data Sets: A Gateway-Based Movement Graph Approach", IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 90-104, January 2010, doi:10.1109/TKDE.2009.61
87 ms
(Ver )