2026-09-19T00:36:51Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/901432022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Samuel Madden.
Vartak, Manasi
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-09-19T21:42:16Z
2014-09-19T21:42:16Z
2014
2014
http://hdl.handle.net/1721.1/90143
890154135
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
25
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 50-52).
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 propose 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".
by Manasi Vartak.
S.M.
52 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Visualizing database queries
Thesis
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