Use of AI in Query Optimization of Relational Databases

2006 ◽  
Author(s):  
Amit Goyal ◽  
Laurentiu Vasiliu ◽  
Brahmananda Sapkota
Keyword(s):  
Query Optimization ◽  
Relational Databases

Adaptive rank-aware query optimization in relational databases

2006 ◽  
Vol 31 (4) ◽  
pp. 1257-1304 ◽  
Author(s):  
Ihab F. Ilyas ◽  
Walid G. Aref ◽  
Ahmed K. Elmagarmid ◽  
Hicham G. Elmongui ◽  
Rahul Shah ◽  
...  
Keyword(s):  
Query Optimization ◽  
Relational Databases

scholarly journals Cost-Based Query Optimization in Centralized Relational Databases

2019 ◽  
Vol 24 (1) ◽  
pp. 42-46
Author(s):  
Nawaraj Paudel ◽  
Jagdish Bhatta
Keyword(s):  
Query Optimization ◽  
Relational Databases ◽  
Query Language ◽  
Database Management System ◽  
Query Plan ◽  
Efficiency Cost ◽  
The Cost ◽  
Disk Access ◽  
The Given ◽  
Relational Database Management

Query optimization is the most significant factor for any centralized relational database management system (RDBMS) that reduces the total execution time of a query. Query optimization is the process of executing a SQL (Structured Query Language) query in relational databases to determine the most efficient way to execute a given query by considering the possible query plans. The goal of query optimization is to optimize the given query for the sake of efficiency. Cost-based query optimization compares different strategies based on relative costs (amount of time that the query needs to run) and selects and executes one that minimizes the cost. The cost of a strategy is just an estimate based on how many estimated CPU and I/O resources that the query will use. In this paper, cost is considered by counting number of disk accesses for each query plan because disk access tends to be the dominant cost in query processing for centralized relational databases.

scholarly journals A PIVOT based Query Optimization Technique for Horizontal View Tables in Relational Databases

2007 ◽  
Vol 14D (2) ◽  
pp. 157-168
Author(s):  
Sung-Hyun Shin ◽  
Yang-Sae Moon ◽  
Jin-Ho Kim ◽  
Gong-Mi Kang
Keyword(s):  
Query Optimization ◽  
Relational Databases ◽  
Optimization Technique ◽  
Horizontal View

DESIGN AND IMPLEMENTATION OF A KNOWLEDGE-BASED QUERY PROCESSOR

1993 ◽  
Vol 02 (02) ◽  
pp. 107-125 ◽  
Author(s):  
NABIL R. ADAM ◽  
ARYYA GANGOPADHYAY ◽  
JAMES GELLER
Keyword(s):  
Query Optimization ◽  
Relational Databases ◽  
Semantic Knowledge ◽  
Database Management System ◽  
Database Schema ◽  
Semantic Query ◽  
Design And Implementation ◽  
Knowledge Based ◽  
User Query ◽  
Semantic Query Optimization

This paper deals with query processing using semantic knowledge in relational databases. The Select-Project-Join (SPJ) conjunctive class of queries are dealt with in this paper. We propose to optimize highly repetitive queries by using semantic transformations in addition to syntactic transformations. Thus, we generate a set of pre-optimized queries. This set contains queries that are semantically equivalent to, syntactically different from, and more efficient to process than the user queries that we started with. The issues we address in this paper are: how to map a user query to a query that is in the set of pre-optimized and already optimized queries, how to search efficiently through the set of pre-optimized queries and set of semantic rules, and how to incorporate new queries to the set of pre-optimized queries, so that the number of queries that can be optimized using this method increases with the passage of time. Furthermore, we suggest some ideas of handling queries that do not have any semantically equivalent counterpart in the set of pre-optimized queries. We have tested the performance of the proposed method. An algorithm for mapping is implemented in Prolog. A database schema is implemented in the INGRES database management system. We have adopted a database schema that is widely used for measuring performance in the semantic query optimization literature.

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