{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/90143"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/90143","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Visualizing database queries","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 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\".","abstract_html":"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".","abstract_has_math":false,"creators":["Vartak, Manasi"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Samuel Madden."],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014","date_published":"2014","updated_at":"2026-07-22T22:22:02Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. 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