Abstract
Nowadays, Radio frequency identification (RFID) has been extensively deployed to retailing, supply chain management, object recognition, object monitoring and tracking and many other fields. Detecting outliers in RFID data streams can help us find abnormal activities and thus avoid disasters. In order to detect outliers in RFID data streams efficiently and effectively, we proposed a fractal based outlier detection algorithm. Firstly, we built a monotone searching space based on the self-similarity of fractal. Then, we proposed two piecewise fractal models for RFID data streams, and presented an outlier detection algorithm based on the piecewise fractal model. Finally, we validated the efficiency and effectiveness of the proposed algorithm by massive experiments.
Highlights
' " ,("'* - $ #- (# &( " ')$$ - " 89H97H CIH@=9FG =B 85H5 GHF95AG LD9F=A9BHG 5B8 7CB7@I
2 =B8&IH 1 2 #& 1 2 #& 1 2 9H7 CK9J9F H
*
Summary
' " ,("'* - $ #- (# &( " ')$$ - " 89H97H CIH@=9FG =B 85H5 GHF95AG LD9F=A9BHG 5B8 7CB7@I 2 =B8&IH 1 2 #& 1 2 #& 1 2 9H7 CK9J9F H
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Online Engineering (iJOE)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
