Since the emergence of Radio Frequency Identi?cation technology (RFID), the community has been promised a cost effective and ef?cient means to identify and track large number of items with relative ease. Unfortutely, due to the unreliable ture of the passive architecture, the RFID revolution has been reduced to a fraction of intended audience due to the anomalies. These anomalies are duplicate, positive and negative readings. While duplicate readings and wrong data (false positive) can be easily identi?ed and recti?ed, that is not the case for false negative or missed readings. To identify missed readings data mining methods can be used. However, due to its vast volume and complex spatio-temporal structure of RFID data, traditiol data mining methods are not necessarily directly applicable. In this paper we propose method to identify possible missed RFID readings by applying association rules data mining method. In empirical study we show that our algorithm is accurate and ef?cient and also we show that it scales well with increased number of rows therefore it is applicable on vast volume on spatio-temporal RFID data.

Presented at Conferences

  • Tenth IASTED International Conference on Artificial Intelligence and Applications (AIA 2010) (2010)

    Innsbruck, Austria