Modeling massive RFID data sets: A gateway-based movement graph approach

Hector Gonzalez*, Jiawei Han, Hong Cheng, Xiaolei Li, Diego Klabjan, Tianyi Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


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.

Original languageEnglish (US)
Article number4798163
Pages (from-to)90-104
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number1
StatePublished - Jan 2010


  • Data models.
  • Data warehousing
  • RFID

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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