{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T16:52:49Z","timestamp":1776099169957,"version":"3.50.1"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2016,11]]},"abstract":"<jats:p>\n            Data visualization is by far the most commonly used mechanism to explore and extract insights from datasets, especially by novice data scientists. And yet, current visual analytics tools are rather limited in their ability to operate on collections of visualizations---by composing, filtering, comparing, and sorting them---to find those that depict desired trends or patterns. The process of visual data exploration remains a tedious process of\n            <jats:italic>trial-and-error.<\/jats:italic>\n            We propose zenvisage, a visual analytics platform for effortlessly finding desired visual patterns from large datasets. We introduce zenvisage's general purpose visual exploration language, ZQL (\"zee-quel\") for specifying the desired visual patterns, drawing from use-cases in a variety of domains, including biology, mechanical engineering, climate science, and commerce. We formalize the expressiveness of ZQL via a visual exploration algebra---an algebra on collections of visualizations---and demonstrate that ZQL is as expressive as that algebra. zenvisage exposes an interactive front-end that supports the issuing of ZQL queries, and also supports interactions that are \"short-cuts\" to certain commonly used ZQL queries. To execute these queries, zenvisage uses a novel ZQL graph-based query optimizer that leverages a suite of optimizations tailored to the goal of processing collections of visualizations in certain pre-defined ways. Lastly, a user survey and study demonstrates that data scientists are able to effectively use zenvisage to eliminate error-prone and tedious exploration and directly identify desired visualizations.\n          <\/jats:p>","DOI":"10.14778\/3025111.3025126","type":"journal-article","created":{"date-parts":[[2017,1,24]],"date-time":"2017-01-24T15:29:41Z","timestamp":1485271781000},"page":"457-468","source":"Crossref","is-referenced-by-count":112,"title":["Effortless data exploration with zenvisage"],"prefix":"10.14778","volume":"10","author":[{"given":"Tarique","family":"Siddiqui","sequence":"first","affiliation":[{"name":"University of Illinois, Urbana-Champaign (UIUC)"}]},{"given":"Albert","family":"Kim","sequence":"additional","affiliation":[{"name":"MIT"}]},{"given":"John","family":"Lee","sequence":"additional","affiliation":[{"name":"University of Illinois, Urbana-Champaign (UIUC)"}]},{"given":"Karrie","family":"Karahalios","sequence":"additional","affiliation":[{"name":"University of Illinois, Urbana-Champaign (UIUC) and Adobe Research"}]},{"given":"Aditya","family":"Parameswaran","sequence":"additional","affiliation":[{"name":"University of Illinois, Urbana-Champaign (UIUC)"}]}],"member":"320","published-online":{"date-parts":[[2016,11]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Airline dataset (http:\/\/stat-computing.org\/dataexpo\/2009\/the-data.html). {Online; accessed 30-Oct-2015}.  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Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks , volume 19 . Academic press , 2014 . M. R. Anderberg. Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks, volume 19. Academic press, 2014."},{"key":"e_1_2_1_11_1","volume-title":"Research design and methods: A process approach","author":"Bordens K. S.","year":"2002","unstructured":"K. S. Bordens and B. B. Abbott . Research design and methods: A process approach . McGraw-Hill , 2002 . K. S. Bordens and B. B. Abbott. Research design and methods: A process approach. McGraw-Hill, 2002."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807286"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1356052.1356053"},{"key":"e_1_2_1_14_1","first-page":"27","volume-title":"Proc. 1996 SiGMOD","volume":"96","author":"Han J.","year":"1996","unstructured":"J. Han : A data mining query language for relational databases . In Proc. 1996 SiGMOD , volume 96 , pages 27 -- 34 , 1996 . J. Han et al. Dmql: A data mining query language for relational databases. In Proc. 1996 SiGMOD, volume 96, pages 27--34, 1996."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ANZIIS.1994.396988"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009816913055"},{"key":"e_1_2_1_17_1","volume-title":"IEEE TVCG","author":"Wongsuphasawat K.","year":"2015","unstructured":"K. Wongsuphasawat : Exploratory analysis via faceted browsing of visualization recommendations . IEEE TVCG , 2015 . K. Wongsuphasawat et al. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. 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