{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T10:09:09Z","timestamp":1790676549581,"version":"4.1.0"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2015,9]]},"abstract":"<jats:p>\n            Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose S\n            <jats:sc>ee<\/jats:sc>\n            DB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, S\n            <jats:sc>ee<\/jats:sc>\n            DB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most \"useful\" or \"interesting\". The two major obstacles in recommending interesting visualizations are (a)\n            <jats:italic>scale<\/jats:italic>\n            : evaluating a large number of candidate visualizations while responding within interactive time scales, and (b)\n            <jats:italic>utility<\/jats:italic>\n            : identifying an appropriate metric for assessing interestingness of visualizations. For the former, S\n            <jats:sc>ee<\/jats:sc>\n            DB introduces\n            <jats:italic>pruning optimizations<\/jats:italic>\n            to quickly identify high-utility visualizations and\n            <jats:italic>sharing optimizations<\/jats:italic>\n            to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviation-based metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement S\n            <jats:sc>ee<\/jats:sc>\n            DB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to\n            <jats:italic>multiple orders of magnitude speedup<\/jats:italic>\n            on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics.\n          <\/jats:p>","DOI":"10.14778\/2831360.2831371","type":"journal-article","created":{"date-parts":[[2015,9,30]],"date-time":"2015-09-30T12:16:36Z","timestamp":1443615396000},"page":"2182-2193","source":"Crossref","is-referenced-by-count":152,"title":["S\n            <scp>ee<\/scp>\n            DB"],"prefix":"10.14778","volume":"8","author":[{"given":"Manasi","family":"Vartak","sequence":"first","affiliation":[{"name":"MIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sajjadur","family":"Rahman","sequence":"additional","affiliation":[{"name":"University of Illinois (UIUC)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Madden","sequence":"additional","affiliation":[{"name":"MIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aditya","family":"Parameswaran","sequence":"additional","affiliation":[{"name":"University of Illinois (UIUC)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neoklis","family":"Polyzotis","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2015,9]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"www.tableaupublic.com. {Online","year":"2014","unstructured":"Tableau public , www.tableaupublic.com. {Online ; accessed 3- March - 2014 }. 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