• Corpus ID: 53298673

VizRec: A framework for secure data exploration via visual representation

@article{Stefani2018VizRecAF,
  title={VizRec: A framework for secure data exploration via visual representation},
  author={Lorenzo De Stefani and Leonhard F. Spiegelberg and Tim Kraska and Eli Upfal},
  journal={ArXiv},
  year={2018},
  volume={abs/1811.00602}
}

• L. D. Stefani, Leonhard F. Spiegelberg, +1 author E. Upfal
• Published 1 November 2018
• Computer Science
• ArXiv

Visual representations of data (visualizations) are tools of great importance and widespread use in data analytics as they provide users visual insight to patterns in the observed data in a simple and effective way. However, since visualizations tools are applied to sample data, there is a a risk of visualizing random fluctuations in the sample rather than a true pattern in the data. This problem is even more significant when visualization is used to identify interesting patterns among many possible possibilities, or to identify an interesting deviation in a pair of observations among many possible pairs, as commonly done in visual recommendation systems.
We present VizRec, a framework for improving the performance of visual recommendation systems by quantifying the statistical significance of recommended visualizations. The proposed methodology allows to control the probability of misleading visual recommendations using both classical statistical testing procedures and a novel application of the Vapnik Chervonenkis (VC) dimension method which is a fundamental concept in statistical learning theory.