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100959 |
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|a dc
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|a Vartak, Manasi
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Vartak, Manasi
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|a Madden, Samuel R.
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|a Parameswaran, Aditya
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|a Parameswaran, Aditya
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|a Polyzotis, Neoklis
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|a Madden, Samuel R.
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|a SeeDB: automatically generating query visualizations
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|b Association for Computing Machinery (ACM),
|c 2016-01-20T18:41:32Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/100959
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|a 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.
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|a en_US
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|a Article
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|t Proceedings of the VLDB Endowment
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