An efficient RFID data cleaning method based on wavelet density estimation

Yaozong Liu, Hong Zhang, Fawang Han, Jun Tan

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

A large number of noise are usually carried in the original RFID data and need to be cleaned up before further processing. Outlier detection is an effective method for RFID data cleaning. In this paper, a point probability data model was proposed to describe the uncertain RFID data streams. The wavelet density threshold was incorporated in this method to adaptively detect the outliers in the sliding window by utilizing the multi-scale and multi-granularity characteristics of wavelet density estimation. The process of outlier detection for RFID data streams was discussed in depth. It was shown that, compared with the existing kernel density estimation algorithm, our method had higher efficiency and precision for the uncertain data streams of RFID data cleaning.

Original languageEnglish (US)
Pages (from-to)10-14
Number of pages5
JournalJournal of Digital Information Management
Volume13
Issue number1
StatePublished - Jan 1 2015

Keywords

  • Data cleaning
  • Outlier
  • RFID data streams
  • Uncertain
  • Wavelet density estimation (WDE)

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Library and Information Sciences

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