2026-09-19T02:59:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1126652026-06-06T00:54:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Michael Stonebraker.
Duan, Peitong
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2017-12-08T21:20:37Z
2017-12-08T21:20:37Z
2017
2017
http://hdl.handle.net/1721.1/112665
1014123603
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).
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.
by Peitong Duan.
M.Eng.
74 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Beagle : automated extraction and interpretation of visualizations from the web
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="7630f921-42e4-44da-b373-18b805961955">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Beagle : automated extraction and interpretation of visualizations from the web</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2017</PublicationDate>
<Authors>
<Author>
<DisplayName>Duan, Peitong</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>