2026-09-19T01:38:55Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1060042026-06-06T00:55:33Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Samuel Madden. Nguyen, Qui T Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2016-12-22T15:18:13Z 2016-12-22T15:18:13Z 2015 2015 http://hdl.handle.net/1721.1/106004 965799432 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. 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 59-62). Data partitioning can significantly improve query performance in distributed database systems. Most proposed data partitioning techniques choose the partitioning based on a particular expected query workload or use a simple upfront scheme, such as uniform range partitioning or hash partitioning on a key. However, these techniques do not adequately address the case where the query workload is ad-hoc and unpredictable, as in many analytic applications. The HYPER-PARTITIONING system aims to ll that gap, by using a novel space-partitioning tree on the space of possible attribute values to dene partitions incorporating all attributes of a dataset. The system creates a robust upfront partitioning tree, designed to benet all possible queries, and then adapts it over time in response to the actual workload. This thesis evaluates the robustness of the upfront hyper-partitioning algorithm, describes the implementation of the overall HYPER-PARTITIONING system, and shows how hyper-partitioning improves the performance of both selection and join queries. by Qui T. Nguyen. M.Eng. 62 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. Robust data partitioning for ad-hoc query processing Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="b1f284e4-b03a-4fa7-a578-428eb9422421"> <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>Robust data partitioning for ad-hoc query processing</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Nguyen, Qui T</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>Data partitioning can significantly improve query performance in distributed database systems. Most proposed data partitioning techniques choose the partitioning based on a particular expected query workload or use a simple upfront scheme, such as uniform range partitioning or hash partitioning on a key. However, these techniques do not adequately address the case where the query workload is ad-hoc and unpredictable, as in many analytic applications. The HYPER-PARTITIONING system aims to ll that gap, by using a novel space-partitioning tree on the space of possible attribute values to dene partitions incorporating all attributes of a dataset. The system creates a robust upfront partitioning tree, designed to benet all possible queries, and then adapts it over time in response to the actual workload. This thesis evaluates the robustness of the upfront hyper-partitioning algorithm, describes the implementation of the overall HYPER-PARTITIONING system, and shows how hyper-partitioning improves the performance of both selection and join queries.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>