Exploring learned indexes for approximate query processing and visual interfaces
Name
1128830295-MIT.pdf
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1.37 MB
Format
Adobe PDF
Checksum (MD5)
66488ce693a26badb7863bec3efa2647
Author(s)
Sedlar, Katharine N.
Advisor(s)
Tim Kraska.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Learned index structures are a promising new direction for improving data access. They offer the ability to do fast lookups in very large data sets, such as the kind needed for visual interfaces, without taking up huge amounts of memory. This paper discusses the extension of research with learned index structures as applied to approximating range search queries for visualization, some of the unexpected theoretical challenges this task brings up, and how learned index structures compare with other modern techniques for fast visualization.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 40-41).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
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