Warehousing and analyzing massive RFID data sets

Hector Gonzalez*, Jiawei Han, Xiaolei Li, Diego Klabjan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

227 Scopus citations

Abstract

Radio Frequency Identification (RFID) applications are set to play an essential role in object tracking and supply chain management systems. In the near future, it is expected that every major retailer will use RFID systems to track the movement of products from suppliers to warehouses, store backrooms and eventually to points of sale. The volume of information generated by such systems can be enormous as each individual item (a pallet, a case, or an SKU) will leave a trail of data as it moves through different locations. As a departure from the traditional data cube, we propose a new warehousing model that preserves object transitions while providing significant compression and path-dependent aggregates, based on the following observations: (1) items usually move together in large groups through early stages in the system (e.g., distribution centers) and only in later stages (e.g., stores) do they move in smaller groups, and (2) although RFID data is registered at the primitive level, data analysis usually takes place at a higher abstraction level. Techniques for summarizing and indexing data, and methods for processing a variety of queries based on this framework are developed in this study. Our experiments demonstrate the utility and feasibility of our design, data structure, and algorithms.

Original languageEnglish (US)
Title of host publicationProceedings of the 22nd International Conference on Data Engineering, ICDE '06
Pages83
Number of pages1
DOIs
StatePublished - 2006
Event22nd International Conference on Data Engineering, ICDE '06 - Atlanta, GA, United States
Duration: Apr 3 2006Apr 7 2006

Publication series

NameProceedings - International Conference on Data Engineering
Volume2006
ISSN (Print)1084-4627

Other

Other22nd International Conference on Data Engineering, ICDE '06
CountryUnited States
CityAtlanta, GA
Period4/3/064/7/06

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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