Assisting data exploration via in-situ adaptive visualizations

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.

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

2020

Author dc:creator

• Kim, Jaewoo

Contributors dc:contributor

• Parameswaran, Aditya

• Data analysis
• Visualizations
• Recommendations
• Scientific Applications

Statement dc:rights

• Copyright 2020 Jaewoo Kim

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108046

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108046