Comparison of Computational Notebook Platforms for Interactive Visual Analytics: Case Study of Andromeda Implementations
| dc.contributor.author | Liu, Han | en |
| dc.contributor.committeechair | North, Christopher L. | en |
| dc.contributor.committeemember | Shaffer, Clifford A. | en |
| dc.contributor.committeemember | Fox, Edward A. | en |
| dc.contributor.department | Computer Science and Applications | en |
| dc.date.accessioned | 2022-09-23T08:00:28Z | en |
| dc.date.available | 2022-09-23T08:00:28Z | en |
| dc.date.issued | 2022-09-22 | en |
| 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. Laying the groundwork for future developers, advice is also given on architecting better notebook platforms. | en |
| dc.description.abstractgeneral | 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. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:35442 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/111975 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Visual Analytics | en |
| dc.subject | Data Science | en |
| dc.subject | Computational Notebooks | en |
| dc.title | Comparison of Computational Notebook Platforms for Interactive Visual Analytics: Case Study of Andromeda Implementations | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Science and Applications | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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