VizCertify: A Framework for Secure Visual Data Exploration
© 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. Howev...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/132285 |
| _version_ | 1811070335730057216 |
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| author | De Stefani, Lorenzo Spiegelberg, Leonhard F Upfal, Eli Kraska, Tim |
| author_facet | De Stefani, Lorenzo Spiegelberg, Leonhard F Upfal, Eli Kraska, Tim |
| author_sort | De Stefani, Lorenzo |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T08:34:20Z |
| format | Article |
| id | mit-1721.1/132285 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:34:20Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1322852021-09-21T04:05:11Z VizCertify: A Framework for Secure Visual Data Exploration De Stefani, Lorenzo Spiegelberg, Leonhard F Upfal, Eli Kraska, Tim © 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. 2021-09-20T18:21:40Z 2021-09-20T18:21:40Z 2021-01-11T17:18:07Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/132285 en 10.1109/DSAA.2019.00039 Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) other univ website |
| spellingShingle | De Stefani, Lorenzo Spiegelberg, Leonhard F Upfal, Eli Kraska, Tim VizCertify: A Framework for Secure Visual Data Exploration |
| title | VizCertify: A Framework for Secure Visual Data Exploration |
| title_full | VizCertify: A Framework for Secure Visual Data Exploration |
| title_fullStr | VizCertify: A Framework for Secure Visual Data Exploration |
| title_full_unstemmed | VizCertify: A Framework for Secure Visual Data Exploration |
| title_short | VizCertify: A Framework for Secure Visual Data Exploration |
| title_sort | vizcertify a framework for secure visual data exploration |
| url | https://hdl.handle.net/1721.1/132285 |
| work_keys_str_mv | AT destefanilorenzo vizcertifyaframeworkforsecurevisualdataexploration AT spiegelbergleonhardf vizcertifyaframeworkforsecurevisualdataexploration AT upfaleli vizcertifyaframeworkforsecurevisualdataexploration AT kraskatim vizcertifyaframeworkforsecurevisualdataexploration |