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Georgia Institute of Technology

Symbolic Reasoning for Query Verification and Optimization

Abstract

dc:description.abstract

Structured Query Language (SQL) is the most widely used language for interacting with many database management systems (DBMS). Thus, the problems of optimizing and verifying SQL queries are two of the most studied problems in the DBMS community. Traditional techniques for optimizing and verifying SQL queries are based on syntax-driven approaches, which suffer many limitations in terms of effectiveness and efficiency. In this dissertation, I investigate two important problems in query verification and optimization to demonstrate the limitations of syntax-driven techniques: (1) proving query equivalence under set and bag semantics; (2) optimizing queries with learned predicates. I propose to use symbolic reasoning to address the limitations of syntax-driven approaches in these two problems. I first present two techniques for proving query equivalence under set and bag semantics based on symbolic representation. Both approaches are significantly more efficient and effective than the previous state-of-the-art syntax-driven techniques. I then present a novel algorithm that combines symbolic reasoning with machine learning to synthesize new predicates for optimizing queries. This algorithm enables the query optimizer to leverage more optimization rules that it cannot previously apply. This technique significantly speeds up the execution of queries with complex predicates. In conclusion, this thesis proved that using symbolic reasoning can significantly improve the efficiency and effectiveness of techniques for query equivalence verification and query optimization.

Degree

thesis:*
Level thesis:degree_level
Doctoral
Department dc:contributor.department
Computer Science
Grantor dc:publisher
Georgia Institute of Technology
Year dc:date.issued
2020

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Zhou, Qi
Advisors dc:contributor.advisor
  • Harris, William
  • Arulraj, Joy
Committee members dc:contributor.committeemember
  • Orso, Alex
  • Regehr, John
  • Navathe, Shamkant B.

Subjects

dc:subject × 1

Rights

Language dc:language.iso
en_US

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1853/64222
OAI identifier oai:identifier
oai:repository.gatech.edu:1853/64222

Chain of custody

source
Harvested from
Georgia Tech
Base URL
repository.gatech.edu/server/oai/request
Last updated
2026-07-27
Source record
OAI-PMH GetRecord
citation

Zhou, Qi. Symbolic Reasoning for Query Verification and Optimization. Doctoral thesis, Georgia Institute of Technology, 2020. http://hdl.handle.net/1853/64222