Towards Visualization Recommendation Systems
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dsia.pdf
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Accepted version
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500.75 KB
Format
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Author(s)
Vartak, Manasi
Huang, Silu
Siddiqui, Tarique
Madden, Samuel R
Parameswaran, Aditya
Date Issued
December 2016
Journal
ACM SIGMOD Record
Publisher
Association for Computing Machinery (ACM)
Citation
Vartak, Manasi, Silu Huang, Tarique Siddiqui, Samuel Madden and Aditya Parameswaran. "Towards Visualization Recommendation Systems." ACM SIGMOD Record, 45 (4), December 2016, 34-39.
Version
Author's final manuscript
Abstract
Data visualization is often used as the first step while performing a variety of analytical tasks. With the advent of large, high-dimensional datasets and significant interest in data science, there is a need for tools that can support rapid visual analysis. In this paper we describe our vision for a new class of visualization systems, namely visualization recommendation systems, that can automatically identify and interactively recommend visualizations relevant to an analytical task. We detail the key requirements and design considerations for a visualization recommendation system. We also identify a number of challenges in realizing this vision and describe some approaches to address them.
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/10.1145/3092931.3092937
