VizCertify: A Framework for Secure Visual Data Exploration
Name
VizCertify_DSAA.pdf
Description
Accepted version
Size
499.49 KB
Format
Unknown
Checksum (MD5)
bb05267497606b2fca17a5bd44962ac8
Author(s)
De Stefani, Lorenzo
Spiegelberg, Leonhard F
Upfal, Eli
Kraska, Tim
Date Issued
2019
Journal
Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
© 2019 IEEE. Recently, there have been several proposals to develop visual recommendation systems. The most advanced systems aim to recommend visualizations, which help users to find new correlations or identify an interesting deviation based on the current context of the user's analysis. However, when recommending a visualization to a user, there is an inherent risk to visualize random fluctuations rather than solely true patterns: a problem largely ignored by current techniques. In this paper, we present VizCertify, a novel framework to improve 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 towards visualization recommendation which results in an effective criterion to decide whether a recommendation corresponds to a true phenomenon or not.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/DSAA.2019.00039
