Beagle : automated extraction and interpretation of visualizations from the web

In this paper, we present Beagle, an automated data collection system to mine the web for SVG-based visualization images, label them with their corresponding visualization type (i.e., bar, scatter, pie, etc.), and make them available as a queryable data store. The key idea behind Beagle is a new SVG-based classification design to more effectively label visualizations rendered in a browser. Furthermore, Beagle is designed from the ground up to be extendable and modifiable in a straightforward way, to anticipate when new artifacts appear on the web, such as new JavaScript libraries, new visualization types, and better browser support for SVG. We evaluated Beagle's classification techniques on multiple collections of SVG-based visualizations extracted from the web, and found that Beagle provides a significant boost in accuracy compared to existing classification techniques, across a wide variety of visualization types.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Duan, Peitong

Advisor dc:contributor.advisor

• Michael Stonebraker.

• Electrical Engineering and Computer Science.

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

http://hdl.handle.net/1721.1/112665

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/112665