scholarly journals SQL Query Optimization Techniques

IJARCCE ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 87-90
Author(s):  
Priyanka R. Munot ◽  
Dipali R. Patil ◽  
Kajal P. Pathak
Keyword(s):  
Query Optimization ◽  
Optimization Techniques ◽  
Sql Query

Query Optimization in the Databases Distributed by Means of Product Equivalence Relations

1988 ◽  
Vol 11 (3) ◽  
pp. 241-265
Author(s):  
W. Marek ◽  
C. Rauszer
Keyword(s):  
Query Optimization ◽  
Selection Process ◽  
Distributed Databases ◽  
Optimization Techniques ◽  
Equivalence Relations ◽  
Database Operations ◽  
By Products

In this paper, we address the problem of query optimization in distributed databases. We show that horizontal partitions of databases, generated by products of equivalence relations, induce optimization techniques for the basic database operations (i.e., the selection, projection, and join operators). In the case of selection, our method allows for restriction of the number of blocks to be searched in the selection process and subsequent simplification of the selection formula at each block. For the natural join operation, we propose an algorithm that reduces the computation of fragments. Proofs of the correctness of our algorithms are also included.

MPP SQL Query Optimization with RTCG

2018 ◽  
pp. 228-249
Author(s):  
K. T. Sridhar ◽  
M. A. Sakkeer ◽  
Shiju Andrews ◽  
Jimson Johnson
Keyword(s):  
Query Optimization ◽  
Sql Query

Composite Event Processing for Data Streams and Domain Knowledge

2011 ◽  
Vol 219-220 ◽  
pp. 927-931
Author(s):  
Jun Qiang Liu ◽  
Xiao Ling Guan
Keyword(s):  
Query Optimization ◽  
Data Streams ◽  
Domain Knowledge ◽  
Semantic Information ◽  
Query Language ◽  
Processing System ◽  
Optimization Techniques ◽  
Research Attention ◽  
Composite Event ◽  
Solid Foundation

In recent years the processing of composite event queries over data streams has attracted a lot of research attention. Traditional database techniques were not designed for stream processing system. Furthermore, example continuous queries are often formulated in declarative query language without specifying the semantics. To overcome these deficiencies, this article presents the design, implementation, and evaluation of a system that executes data streams with semantic information. Then, a set of optimization techniques are proposed for handling query. So, our approach not only makes it possible to express queries with a sound semantics, but also provides a solid foundation for query optimization. Experiment results show that our approach is effective and efficient for data streams and domain knowledge.

Document SQL (DSQL)

2011 ◽  
pp. 316-344
Author(s):  
Arijit Sengupta ◽  
V. Ramesh
Keyword(s):  
Query Optimization ◽  
Ad Hoc ◽  
Query Language ◽  
Optimization Techniques ◽  
Order Logic ◽  
First Order Logic ◽  
Tree Structures ◽  
Easy Integration ◽  
Query Semantics ◽  
Theoretical Foundations

This chapter presents DSQL, a conservative extension of SQL, as an ad-hoc query language for XML. The development of DSQL follows the theoretical foundations of first order logic, and uses common query semantics already accepted for SQL. DSQL represents a core subset of XQuery that lends well to query optimization techniques; while at the same time allows easy integration into current databases and applications that use SQL. The intent of DSQL is not to replace XQuery, the current W3C recommended XML query language, but to serve as an ad-hoc querying frontend to XQuery. Further, the authors present proofs for important query language properties such as complexity and closure. An empirical study comparing DSQL and XQuery for the purpose of ad-hoc querying demonstrates that users perform better with DSQL for both flat and tree structures, in terms of both accuracy and efficiency.

Query Formation and Information Retrieval with Ontology

2007 ◽  
pp. 310-323
Author(s):  
Sheng-Uei Guan
Keyword(s):  
Genetic Algorithms ◽  
Information Retrieval ◽  
Query Optimization ◽  
Formation Process ◽  
Retrieval System ◽  
Mobile Commerce ◽  
Information Retrieval System ◽  
Optimization Techniques ◽  
Tree Structure ◽  
Retrieval Scheme

This chapter presents an ontology-based query formation and information retrieval system under the mobile commerce (m-commerce) agent framework. A query formation approach that combines the usage of ontology and keywords is implemented. This approach takes advantage of the tree structure in ontology to form queries visually and efficiently. It also uses additional aids such as keywords to complete the query formation process more efficiently. The proposed information retrieval scheme focuses on using genetic algorithms (GAs) to improve computational effectiveness. Other query optimization techniques used include query restructuring by logical terms and numerical constraints replacement.

A Query Beehive Algorithm for Data Warehouse Buffer Management and Query Scheduling

2014 ◽  
Vol 10 (3) ◽  
pp. 34-58 ◽  
Author(s):  
Amira Kerkad ◽  
Ladjel Bellatreche ◽  
Pascal Richard ◽  
Carlos Ordonez ◽  
Dominique Geniet
Keyword(s):  
Query Optimization ◽  
Buffer Management ◽  
Optimization Techniques ◽  
Materialized Views ◽  
Star Schema ◽  
Joint Problem ◽  
Fixed Order ◽  
Pass Through ◽  
Np Hardness ◽  
Query Scheduling

Analytical queries, like those used in data warehouses and OLAP, are generally interdependent. This is due to the fact that the database is usually modeled with a denormalized star schema or its variants, where most queries pass through a large central fact table. Such interaction has been largely exploited in query optimization techniques such as materialized views. Nevertheless, such approaches usually ignore buffer management and assume queries have a fixed order and are known in advance. We believe such assumptions are too strong and thus they need to be revisited and simplified. In this paper, we study the combination of two problems: buffer management and query scheduling, in both static and dynamic scenarios. We present an NP-hardness study of the joint problem, highlighting its complexity. We then introduce a new and highly efficient algorithm inspired by a beehive. We conduct an extensive experimental evaluation on a real DBMS showing the superiority of our algorithm compared to previous ones as well as its excellent scalability.

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