Knowledge and Information Systems

, Volume 44, Issue 2, pp 253–277 | Cite as

A masking index for quantifying hidden glitches

  • Laure Berti-ÉquilleEmail author
  • Ji Meng Loh
  • Tamraparni Dasu
Regular Paper


Data glitches are errors in a dataset. They are complex entities that often span multiple attributes and records. When they co-occur in data, the presence of one type of glitch can hinder the detection of another type of glitch. This phenomenon is called masking. In this paper, we define two important types of masking and propose a novel, statistically rigorous indicator called masking index for quantifying the hidden glitches. We outline four cases of masking: outliers masked by missing values, outliers masked by duplicates, duplicates masked by missing values, and duplicates masked by outliers. The masking index is critical for data quality profiling and data exploration. It enables a user to measure the extent of masking and hence the confidence in the data. In this sense, it is a valuable data quality index for choosing an anomaly detection method that is best suited for the glitches that are present in any given dataset. We demonstrate the utility and effectiveness of the masking index by intensive experiments on synthetic and real-world datasets.


Anomaly detection Masking Duplicate record identification  Missing values Outlier detection 


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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Laure Berti-Équille
    • 1
    • 2
    Email author
  • Ji Meng Loh
    • 3
  • Tamraparni Dasu
    • 4
  1. 1.IRD ESPACE DEVMontpellierFrance
  2. 2.Qatar Computing Research InstituteWest Bay, DohaQatar
  3. 3.Department of Mathematical SciencesNew Jersey Institute of TechnologyNewarkUSA
  4. 4.AT&T Labs-ResearchBedminsterUSA

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