{"id":1915,"date":"2022-01-03T14:20:59","date_gmt":"2022-01-03T19:20:59","guid":{"rendered":"https:\/\/groups.cs.umass.edu\/nmahyar\/?p=1915"},"modified":"2022-01-03T14:20:59","modified_gmt":"2022-01-03T19:20:59","slug":"visualizing-dimension-coverage","status":"publish","type":"post","link":"https:\/\/groups.cs.umass.edu\/nmahyar\/2022\/01\/03\/visualizing-dimension-coverage\/","title":{"rendered":"Visualizing Dimension Coverage"},"content":{"rendered":"\n<h1 class=\"has-medium-font-size wp-block-heading\"><strong><strong><strong>Visualizing Dimension Coverage<\/strong><\/strong><\/strong><\/h1>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"578\" src=\"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2022\/01\/Screenshot-from-2022-01-03-11-49-35-1024x578.png\" alt=\"Screenshot of the visual analysis prototype with three components: (A) A Scented View reveals the dimension coverage information\nusing scented widgets. Bar charts to the left of each dimension name indicate how frequently each has been investigated. Co-investigated\ndimensions are revealed through colouring (blue) when one or more target dimensions are selected (orange). Arrows indicate dimensions\nincluded in the currently displayed chart. (B) Visualization panel. (C) Sequence View shows a chronologically-ordered list of created charts.\" class=\"wp-image-1904\" srcset=\"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2022\/01\/Screenshot-from-2022-01-03-11-49-35-1024x578.png 1024w, https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2022\/01\/Screenshot-from-2022-01-03-11-49-35-300x169.png 300w, https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2022\/01\/Screenshot-from-2022-01-03-11-49-35-768x433.png 768w, https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2022\/01\/Screenshot-from-2022-01-03-11-49-35.png 1283w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n\n<p style=\"text-align: justify\"><strong>Led by:\u00a0 <\/strong>Ali Sarvghad, Melanie Tory, and Narges Mahyar<\/p>\n\n\n\n<p><strong>Related Links:<\/strong><br><a href=\"https:\/\/groups.cs.umass.edu\/wp-content\/uploads\/sites\/8\/2018\/08\/2016-Visualizing-Dimension-Coverage-to-Support-Exploratory-Analysis.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><img decoding=\"async\" width=\"2000\" height=\"2000\" class=\"wp-image-1508\" style=\"width: 18px\" src=\"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2020\/12\/pdf.png\" alt=\"\" srcset=\"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2020\/12\/pdf.png 2000w, https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2020\/12\/pdf-300x300.png 300w, https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2020\/12\/pdf-1024x1024.png 1024w, https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2020\/12\/pdf-150x150.png 150w, https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2020\/12\/pdf-768x768.png 768w, https:\/\/groups.cs.umass.edu\/nmahyar\/wp-content\/uploads\/sites\/8\/2020\/12\/pdf-1536x1536.png 1536w\" sizes=\"(max-width: 2000px) 100vw, 2000px\" \/> &#8220;Visualizing Dimension Coverage to Support Exploratory Analysis&#8221;<\/a><\/p>\n\n\n\n<p>Data analysis involves constantly formulating and testing new hypotheses and questions about data. When dealing with a new dataset, especially one with many dimensions, it can be cumbersome for the analyst to clearly remember which aspects of the data have been investigated (i.e., visually examined for patterns, trends, outliers etc.) and which combinations have not. Yet this information is critical to help the analyst formulate new questions that they have not already answered. We observe that for tabular data, questions are typically comprised of varying combinations of data dimensions (e.g., what are the trends of Sales and Profit for different Regions?). We propose representing analysis history from the angle of dimension coverage (i.e., which data dimensions have been investigated and in which combinations). We use scented widgets [30] to incorporate dimension coverage of the analysts\u2019 past work into interaction widgets of a visualization tool. We demonstrate how this approach can assist analysts with the question formation process. Our approach extends the concept of scented widgets to reveal aspects of one\u2019s own analysis history, and offers a different perspective on one\u2019s past work than typical visualization history tools. Results of our empirical study showed that participants with access to embedded dimension coverage information relied on this information when formulating questions, asked more questions about the data, generated more top-level findings, and showed greater breadth of their analysis without sacrificing<br>depth.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Visualizing Dimension Coverage Led by:\u00a0 Ali Sarvghad, Melanie Tory, and Narges Mahyar Related Links: &#8220;Visualizing Dimension Coverage to Support Exploratory Analysis&#8221; Data analysis involves constantly formulating and testing new hypotheses and questions about data. When dealing with a new dataset, especially one with many dimensions, it can be cumbersome for the analyst to clearly remember &hellip; <a href=\"https:\/\/groups.cs.umass.edu\/nmahyar\/2022\/01\/03\/visualizing-dimension-coverage\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Visualizing Dimension Coverage<\/span> <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":52,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"aside","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-1915","post","type-post","status-publish","format-aside","hentry","category-project-pages","post_format-post-format-aside"],"_links":{"self":[{"href":"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-json\/wp\/v2\/posts\/1915","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-json\/wp\/v2\/users\/52"}],"replies":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-json\/wp\/v2\/comments?post=1915"}],"version-history":[{"count":1,"href":"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-json\/wp\/v2\/posts\/1915\/revisions"}],"predecessor-version":[{"id":1916,"href":"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-json\/wp\/v2\/posts\/1915\/revisions\/1916"}],"wp:attachment":[{"href":"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-json\/wp\/v2\/media?parent=1915"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-json\/wp\/v2\/categories?post=1915"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/nmahyar\/wp-json\/wp\/v2\/tags?post=1915"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}