Exploring learned indexes for approximate query processing and visual interfaces
Author(s)Sedlar, Katharine N.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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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.
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, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 40-41).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.