2026-09-19T04:57:16Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/852302026-06-06T00:55:43Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Piotr Indyk and Samuel Madden.
Turmukhametova, Aizana
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-03-05T15:56:42Z
2014-03-05T15:56:42Z
2013
2013
http://hdl.handle.net/1721.1/85230
871037170
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
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 PDF version of thesis.
Includes bibliographical references (pages 49-51).
This thesis addresses the problem of diverse sampling as a dispersion problem and proposes solutions that are optimized for large streaming data. Finding the optimal solution to the dispersion problem is NP-hard. Therefore, existing and proposed solutions are approximation algorithms. This work evaluates the performance of dierent algorithms in practice and compares them to the theoretical guarantees.
by Aizana Turmukhametova.
M.Eng.
51 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Diverse sampling of streaming data
Thesis
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