{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/111975"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/111975","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Comparison of Computational Notebook Platforms for Interactive Visual Analytics: Case Study of Andromeda Implementations","abstract":"Existing notebook platforms have different capabilities for supporting visual analytics use. It is not clear which platform to choose for implementing visual analytics notebooks. In this work, we investigated the problem using Andromeda, an interactive dimension reduction algorithm, and implemented it using three different notebook platforms: 1) Python-based Jupyter Notebook, 2) JavaScript-based Observable Notebook, and 3) Jupyter Notebook embedding both Python (data science use) and JavaScript (visual analytics use). We also made comparisons for all the notebook platforms via a case study based on metrics such as programming difficulty, notebook organization, interactive performance, and UI design choice. Furthermore, guidelines are provided for data scientists to choose one notebook platform for implementing their visual analytics notebooks in various situations. Laying the groundwork for future developers, advice is also given on architecting better notebook platforms.","abstract_html":"Existing notebook platforms have different capabilities for supporting visual analytics use. It is not clear which platform to choose for implementing visual analytics notebooks. In this work, we investigated the problem using Andromeda, an interactive dimension reduction algorithm, and implemented it using three different notebook platforms: 1) Python-based Jupyter Notebook, 2) JavaScript-based Observable Notebook, and 3) Jupyter Notebook embedding both Python (data science use) and JavaScript (visual analytics use). We also made comparisons for all the notebook platforms via a case study based on metrics such as programming difficulty, notebook organization, interactive performance, and UI design choice. Furthermore, guidelines are provided for data scientists to choose one notebook platform for implementing their visual analytics notebooks in various situations. Laying the groundwork for future developers, advice is also given on architecting better notebook platforms.","abstract_has_math":false,"creators":["Liu, Han"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science and Applications","degree_department":"Computer Science and Applications","school":null,"contributors":[],"advisors":[],"committee_chairs":["North, Christopher L."],"committee_members":["Shaffer, Clifford A.","Fox, Edward A."],"year":2022,"date_issued":"2022-09-22","date_published":"2022-09-22","updated_at":"2026-07-22T22:18:39Z","subjects":["Visual Analytics","Data Science","Computational Notebooks"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:35442"],"render_values":[{"text":"vt_gsexam:35442","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/111975","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["North, Christopher L."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Shaffer, Clifford A.","Fox, Edward A."]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science and Applications"]},{"key":"dc:creator","label":"Author","values":["Liu, Han"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-09-23T08:00:28Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-09-23T08:00:28Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-09-22"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science and Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Visual Analytics","Data Science","Computational Notebooks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:35442"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/111975"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Existing notebook platforms have different capabilities for supporting visual analytics use. It is not clear which platform to choose for implementing visual analytics notebooks. In this work, we investigated the problem using Andromeda, an interactive dimension reduction algorithm, and implemented it using three different notebook platforms: 1) Python-based Jupyter Notebook, 2) JavaScript-based Observable Notebook, and 3) Jupyter Notebook embedding both Python (data science use) and JavaScript (visual analytics use). We also made comparisons for all the notebook platforms via a case study based on metrics such as programming difficulty, notebook organization, interactive performance, and UI design choice. Furthermore, guidelines are provided for data scientists to choose one notebook platform for implementing their visual analytics notebooks in various situations. Laying the groundwork for future developers, advice is also given on architecting better notebook platforms."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Data scientists are interested in developing visual analytics notebooks. However, different notebook platforms have different support for visual analytics components, such as visualizations and user interactions. To investigate which notebook platform to use for visual analytics, we built notebooks based on three different notebook platforms, i.e., Jupyter Notebook (with Python), Observable Notebook (with JavaScript), and Jupyter Notebook (with Python and JavaScript). Based on the implementation and user interactions, we explained why significant differences exist via specific metrics, such as programming difficulty, notebook organization, interactive performance, and the UI design choice. Furthermore, our work will benefit future researchers in choosing suitable notebook platforms for implementing visual analytics notebooks."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Comparison of Computational Notebook Platforms for Interactive Visual Analytics: Case Study of Andromeda Implementations"]}]}],"canonical_facts":{"dc:contributor.committeechair":["North, Christopher L."],"dc:contributor.committeemember":["Shaffer, Clifford A.","Fox, Edward A."],"dc:contributor.department":["Computer Science and Applications"],"dc:creator":["Liu, Han"],"dc:date.accessioned":["2022-09-23T08:00:28Z"],"dc:date.available":["2022-09-23T08:00:28Z"],"dc:date.issued":["2022-09-22"],"dc:description.abstract":["Existing notebook platforms have different capabilities for supporting visual analytics use. It is not clear which platform to choose for implementing visual analytics notebooks. In this work, we investigated the problem using Andromeda, an interactive dimension reduction algorithm, and implemented it using three different notebook platforms: 1) Python-based Jupyter Notebook, 2) JavaScript-based Observable Notebook, and 3) Jupyter Notebook embedding both Python (data science use) and JavaScript (visual analytics use). We also made comparisons for all the notebook platforms via a case study based on metrics such as programming difficulty, notebook organization, interactive performance, and UI design choice. Furthermore, guidelines are provided for data scientists to choose one notebook platform for implementing their visual analytics notebooks in various situations. 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