SeeDB: automatically generating query visualizations
Author(s)Vartak, Manasi; Parameswaran, Aditya; Polyzotis, Neoklis; Madden, Samuel R.
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the VLDB Endowment
Association for Computing Machinery (ACM)
Vartak, Manasi, Samuel Madden, Aditya Parameswaran, and Neoklis Polyzotis. “SeeDB.” Proceedings of the VLDB Endowment 7, no. 13 (August 1, 2014): 1581–1584.
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