Enabling effective visual data exploration for solvent discovery in material science

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Wang, Renxuan

Contributors dc:contributor

• Parameswaran, Aditya

• Data analysis, Visualizations, Scatter Plots, Similarity Metrics, Scientific Applications

Statement dc:rights

• Copyright 2019 Renxuan Wang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/105068

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/105068