Beagle : automated extraction and interpretation of visualizations from the web
Name
1014123603-MIT.pdf
Description
Full printable version
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2.03 MB
Format
Adobe PDF
Checksum (MD5)
cb8b77b48e4f4387e15ad6a7e448b6aa
Author(s)
Duan, Peitong
Advisor(s)
Michael Stonebraker.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted student-submitted PDF version of thesis.
Includes bibliographical references (pages 71-74).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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