Statistical Distortion: Consequences of Data Cleaning

The authors introduce the notion of statistical distortion as an essential metric for measuring the effectiveness of data cleaning strategies. They use this metric to propose a widely applicable yet scalable experimental framework for evaluating data cleaning strategies along three dimensions: glitch improvement, statistical distortion and cost-related criteria. Existing metrics focus on glitch improvement and cost, but not on the statistical impact of data cleaning strategies. They illustrate their framework on real world data, with a comprehensive suite of experiments and analyses.

Provided by: AT&T Labs Topic: Mobility Date Added: Aug 2012 Format: PDF

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