SeeDB: automatically generating query visualizations
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Madden_SEEDB.pdf
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480.19 KB
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Author(s)
Vartak, Manasi
Parameswaran, Aditya
Polyzotis, Neoklis
Madden, Samuel R.
Date Issued
August 2014
Journal
Proceedings of the VLDB Endowment
Publisher
Association for Computing Machinery (ACM)
Citation
Vartak, Manasi, Samuel Madden, Aditya Parameswaran, and Neoklis Polyzotis. “SeeDB.” Proceedings of the VLDB Endowment 7, no. 13 (August 1, 2014): 1581–1584.
Version
Author's final manuscript
Abstract
Data analysts operating on large volumes of data often rely on visualizations to interpret the results of queries. However, finding the right visualization for a query is a laborious and time-consuming task. We demonstrate SeeDB, a system that partially automates this task: given a query, SeeDB explores the space of all possible visualizations, and automatically identifies and recommends to the analyst those visualizations it finds to be most "interesting" or "useful". In our demonstration, conference attendees will see SeeDB in action for a variety of queries on multiple real-world datasets.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.14778/2733004.2733035
