Diverse sampling of streaming data

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2013

Author dc:creator

• Turmukhametova, Aizana

Advisor dc:contributor.advisor

• Piotr Indyk and Samuel Madden.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/85230

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/85230