Title:
Symbolic Reasoning for Query Verification and Optimization
Symbolic Reasoning for Query Verification and Optimization
Author(s)
Zhou, Qi
Advisor(s)
Harris, William
Arulraj, Joy
Arulraj, Joy
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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.
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Date Issued
2020-12-10
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Resource Type
Text
Resource Subtype
Dissertation