{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108046"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108046","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Assisting data exploration via in-situ adaptive visualizations","abstract":"Visual analytics has been widely used by data scientists to shed light on complex problems. Despite the prevalence of many visual analytics tools that empower human decision making with data-driven insights, challenges still exist that hinder users from genuinely capitalizing on insights from visualizations. The two biggest challenges we identify are the lack of task support and disconnected workflow. Visual analytics tools lack task support because they do not actively suggest insights to the users, requiring users to pick each individual step during exploration manually. These tools also suffer from disconnected workflows by keeping interactive exploration via dashboards separate from data preparation and cleaning tools like computational notebooks. To address these challenges, we introduce Lux, a visualization recommendation library that automatically generates useful insights for data exploration, and seamlessly integrates into a user’s data exploration workflow by augmenting the Pandas library. In this thesis, we document the design decisions made and the implementation details of Lux as well as how users can easily unlock intelligent analytical capabilities by adding our library to their code. Furthermore, we share how predecessor visual analytics tools such as Zenvisage that we contributed to guided the development of Lux.","abstract_html":"Visual analytics has been widely used by data scientists to shed light on complex problems. Despite the prevalence of many visual analytics tools that empower human decision making with data-driven insights, challenges still exist that hinder users from genuinely capitalizing on insights from visualizations. The two biggest challenges we identify are the lack of task support and disconnected workflow. Visual analytics tools lack task support because they do not actively suggest insights to the users, requiring users to pick each individual step during exploration manually. These tools also suffer from disconnected workflows by keeping interactive exploration via dashboards separate from data preparation and cleaning tools like computational notebooks. To address these challenges, we introduce Lux, a visualization recommendation library that automatically generates useful insights for data exploration, and seamlessly integrates into a user’s data exploration workflow by augmenting the Pandas library. In this thesis, we document the design decisions made and the implementation details of Lux as well as how users can easily unlock intelligent analytical capabilities by adding our library to their code. Furthermore, we share how predecessor visual analytics tools such as Zenvisage that we contributed to guided the development of Lux.","abstract_has_math":false,"creators":["Kim, Jaewoo"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Parameswaran, Aditya"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T21:58:05Z","date_published":"2020-08-26T21:58:05Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Data analysis","Visualizations","Recommendations","Scientific Applications"],"languages":["en"],"rights":["Copyright 2020 Jaewoo Kim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108046","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Parameswaran, Aditya"]},{"key":"dc:creator","label":"Author","values":["Kim, Jaewoo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T21:58:05Z","2020-05-12","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data analysis","Visualizations","Recommendations","Scientific Applications"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Jaewoo Kim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108046"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Visual analytics has been widely used by data scientists to shed light on complex problems. Despite the prevalence of many visual analytics tools that empower human decision making with data-driven insights, challenges still exist that hinder users from genuinely capitalizing on insights from visualizations. The two biggest challenges we identify are the lack of task support and disconnected workflow. Visual analytics tools lack task support because they do not actively suggest insights to the users, requiring users to pick each individual step during exploration manually. These tools also suffer from disconnected workflows by keeping interactive exploration via dashboards separate from data preparation and cleaning tools like computational notebooks. To address these challenges, we introduce Lux, a visualization recommendation library that automatically generates useful insights for data exploration, and seamlessly integrates into a user’s data exploration workflow by augmenting the Pandas library. In this thesis, we document the design decisions made and the implementation details of Lux as well as how users can easily unlock intelligent analytical capabilities by adding our library to their code. Furthermore, we share how predecessor visual analytics tools such as Zenvisage that we contributed to guided the development of Lux.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Jaewoo Kim, accepted the attached license on 2020-05-12 at 11:58.","The student, Jaewoo Kim, submitted this Thesis for approval on 2020-05-12 at 12:04.","This Thesis was approved for publication on 2020-05-12 at 15:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15348 on 2020-08-25 at 17:14:19","Made available in DSpace on 2020-08-26T21:58:05Z (GMT). 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Visual analytics tools lack task support because they do not actively suggest insights to the users, requiring users to pick each individual step during exploration manually. These tools also suffer from disconnected workflows by keeping interactive exploration via dashboards separate from data preparation and cleaning tools like computational notebooks. To address these challenges, we introduce Lux, a visualization recommendation library that automatically generates useful insights for data exploration, and seamlessly integrates into a user’s data exploration workflow by augmenting the Pandas library. In this thesis, we document the design decisions made and the implementation details of Lux as well as how users can easily unlock intelligent analytical capabilities by adding our library to their code. 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