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Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well.<\/jats:p>","DOI":"10.1177\/1473871618806555","type":"journal-article","created":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T11:13:59Z","timestamp":1540379639000},"page":"251-267","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":84,"title":["DataSite: Proactive visual data exploration with computation of insight-based recommendations"],"prefix":"10.1177","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7542-6095","authenticated-orcid":false,"given":"Zhe","family":"Cui","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA"}]},{"given":"Sriram 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