Automating data visualization through recommendation

Demand for data visualization has exploded in recent years with the increasing availability and use of data across domains. Traditional visualization techniques require users to manually specify visual encodings of data through code or clicks. While manual specification is necessary to create bespoke visualizations, it renders visualization inaccessible to those without technical backgrounds. As a result, visualization recommender systems, which automatically generate results for users to search and select, have gained popularity. Here, I present systems, methods, and data repositories to contextualize and improve visualization recommender systems. The first contribution is DIVE, a publicly available and open source system that combines rule-based recommender systems with manual specification. DIVE integrates state-of-the-art data model inference, visualization, statistical analysis, and storytelling capabilities into a unified workflow.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Hu, Kevin Zeng.

Advisor dc:contributor.advisor

• César Hidalgo.

• Program in Media Arts and Sciences

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123624

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123624