{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105068"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105068","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Enabling effective visual data exploration for solvent discovery in material science","abstract":"Data visualization has become increasingly important in almost all scientific fields. However, current visual analytics tools usually require redundant manual processing, resulting in the visualization process remaining overwhelming and error-prone. Zenvisage automates the process of querying for desired visual patterns, thereby speeding up visual exploration. In this work, we collaborate with material scientists, whose goal is to identify battery solvents with favorable properties while considering economical, physical and chemical tradeoffs in their manufacture. We extend Zenvisage to allow material scientists to compare among subsets of data dynamically and employ non-line chart visualizations to explore their data. In this thesis, we introduce the notion of dynamic class creation, which targets the seamless creation of subsets of data and comparison of properties among them. We address the non-time-series data issue by conducting visual property search queries directly on scatter plots. We implemented polygon-bound queries and drag-and-drop queries for scatter plots, along with two similarity metrics. We also introduce a new approach for material scientists to upload their datasets using scripts. Our work would enable material scientists to get insights more quickly on increasingly large datasets.","abstract_html":"Data visualization has become increasingly important in almost all scientific fields. However, current visual analytics tools usually require redundant manual processing, resulting in the visualization process remaining overwhelming and error-prone. Zenvisage automates the process of querying for desired visual patterns, thereby speeding up visual exploration. In this work, we collaborate with material scientists, whose goal is to identify battery solvents with favorable properties while considering economical, physical and chemical tradeoffs in their manufacture. We extend Zenvisage to allow material scientists to compare among subsets of data dynamically and employ non-line chart visualizations to explore their data. In this thesis, we introduce the notion of dynamic class creation, which targets the seamless creation of subsets of data and comparison of properties among them. We address the non-time-series data issue by conducting visual property search queries directly on scatter plots. We implemented polygon-bound queries and drag-and-drop queries for scatter plots, along with two similarity metrics. We also introduce a new approach for material scientists to upload their datasets using scripts. Our work would enable material scientists to get insights more quickly on increasingly large datasets.","abstract_has_math":false,"creators":["Wang, Renxuan"],"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":2019,"date_issued":"2019-08-23T20:36:05Z","date_published":"2019-08-23T20:36:05Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Data analysis, Visualizations, Scatter Plots, Similarity Metrics, Scientific Applications"],"languages":["en"],"rights":["Copyright 2019 Renxuan Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105068","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":["Wang, Renxuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:36:05Z","2021-08-24T09:15:16Z","2019-04-22","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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, Scatter Plots, Similarity Metrics, 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 2019 Renxuan Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105068"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Data visualization has become increasingly important in almost all scientific fields. 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We implemented polygon-bound queries and drag-and-drop queries for scatter plots, along with two similarity metrics. We also introduce a new approach for material scientists to upload their datasets using scripts. Our work would enable material scientists to get insights more quickly on increasingly large datasets.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Renxuan Wang, accepted the attached license on 2019-04-19 at 19:44.","The student, Renxuan Wang, submitted this Thesis for approval on 2019-04-19 at 19:50.","This Thesis was approved for publication on 2019-04-22 at 13:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13802 on 2019-08-22 at 15:07:37","Made available in DSpace on 2019-08-23T20:36:05Z (GMT). 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However, current visual analytics tools usually require redundant manual processing, resulting in the visualization process remaining overwhelming and error-prone. Zenvisage automates the process of querying for desired visual patterns, thereby speeding up visual exploration. In this work, we collaborate with material scientists, whose goal is to identify battery solvents with favorable properties while considering economical, physical and chemical tradeoffs in their manufacture. We extend Zenvisage to allow material scientists to compare among subsets of data dynamically and employ non-line chart visualizations to explore their data. In this thesis, we introduce the notion of dynamic class creation, which targets the seamless creation of subsets of data and comparison of properties among them. We address the non-time-series data issue by conducting visual property search queries directly on scatter plots. We implemented polygon-bound queries and drag-and-drop queries for scatter plots, along with two similarity metrics. We also introduce a new approach for material scientists to upload their datasets using scripts. Our work would enable material scientists to get insights more quickly on increasingly large datasets.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Renxuan Wang, accepted the attached license on 2019-04-19 at 19:44.","The student, Renxuan Wang, submitted this Thesis for approval on 2019-04-19 at 19:50.","This Thesis was approved for publication on 2019-04-22 at 13:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13802 on 2019-08-22 at 15:07:37","Made available in DSpace on 2019-08-23T20:36:05Z (GMT). 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