Query Optimization Using Genetic Algorithms

Sinha, Manish Kumar; Chande, Swati V.

doi:10.3923/rjit.2010.139.144

Cited by 11 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the foremost objectives of Genetic Algorithm [26] is to provide best or nearly optimal solution from fixed and finite sized population. It is a probabilistic [20] technique based on the concept of natural selection. In simple words the concept of Genetic Algorithm is derived from nature and was first proposed by John Holland [22] [24].…”

Section: Analysis Of Dss Queries Using Genetic Algorithmmentioning

confidence: 99%

Stochastic Analysis of DSS Queries for a Distributed Database Design

Sharma¹,

Singh²,

Singh³

et al. 2013

IJCA

View full text Add to dashboard Cite

Optimization of query in distributed database system is one of the dominant subjects in the field of database theory. Depending upon the placement of data a query can be described as centralized or distributed query. The processing of distributed query is entirely different from the centralized query as in the former case the data is distributed over number of sites. Decision Support System Query (DSSQ) is one of the decisive types of distributed query. DSS queries are complex and time consuming in nature. Due to the decentralization of data and the complexity of query, it becomes mandatory to optimize the DSS query in distributed database system. In this work an effort is made to find an optimal DSS sub query allocation plan in distributed environment stochastically using Genetic Algorithm. The queries are designed on the basis of one of the benchmark of DSS query as given by TPC-DS. The DSS queries are optimized on the basis of Total Cost. The use of Genetic Algorithm has significantly expedited the process of DSS query optimization. The effect of varying communication cost over Total Cost of system resources is also observed.

show abstract

Section: Analysis Of Dss Queries Using Genetic Algorithmmentioning

confidence: 99%

Stochastic Analysis of DSS Queries for a Distributed Database Design

Sharma¹,

Singh²,

Singh³

et al. 2013

IJCA

View full text Add to dashboard Cite

show abstract

“…The basic idea of ant heuristics is the natural behavior of ants that succeed in finding the shortest paths from their nest to food sources. They communicate via a collective memory consisting of pheromone trails Chande & Sinha 2010;Pan & Wang 2009). As ants have weak global insight into their environment, they move randomly when no pheromone is available.…”

Section: Introductionmentioning

confidence: 99%

SQL Optimization in Oracle using Hybrid Genetic and Ant Colony Algorithm

Kusi,

Sinkemana,

Pandey

et al. 2022

Nepal Journal of Science and Technology

View full text Add to dashboard Cite

In this paper, the input user Structured Query Language (SQL) query is converted into an optimized SQL query using a hybrid algorithm. The main aim is to reduce query execution time using PHP language and oracle database. These performance has been evaluated using different performance metrics: Cost of individuals, Query execution time. The hybrid algorithm method combines the evolutionary effect of the Genetic Algorithm (GA) and the cooperative effect of Ant Colony Optimization (ACO). A GA with a great global converging rate aims to produce an initial optimum for allocating initial pheromones of ACO. An ACO with great parallelism and effective feedback is then served to obtain the optimal solution. A fused algorithm of a GA and ACO to solve SQL optimization problems is an innovative solution that presents a clear methodological contribution to the optimization algorithm. In the simulation result, we found the algorithm of a GA and ACO to solve SQL optimization problems in Oracle. It is an innovative solution that presents a clear methodological contribution to the optimization algorithm.

show abstract

“…Join processing records of the relations are on the whole expensive operations occurred in a database management system [1]. So, all query optimization algorithms firstly deal with joins [2]. In the previous years, many join techniques have been designed for better processing of join operation [3] and [4].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach of Block Nested Loop Algorithm Based on Rate of Block Transfer

Shukla¹,

Arora²,

Pandey³

et al. 2011

IJCA

View full text Add to dashboard Cite

In this paper, an approach has been proposed that makes the processing of join operation in database systems more efficient. In join operation processing relations that take part in the join process are required to be transferred to the main memory (RAM) from hard disk. In join operation processing when block nested loop algorithm is used to perform join between relations and multiple blocks of the relations that take part in the joining process are transferred from hard disk to main memory than in this case the main memory buffer allotted to the blocks of relation. Using this approach, multiple blocks are transferred for the relations that participates in the join operation processing, instead of transferring blocks one by one for each relation (or multiple blocks for one relation) without worrying about the large and small databases size. When this new approach is applied, the rate of block transfer during join operation processing using block nested loop algorithm get minimizes and join query processing become efficient, without loosing the level of complexity of the previous algorithms of block nested loop join(BNLJ). General TermsAlgorithm for join operation processing based on rate of block transfer.

show abstract

Query Optimization Using Genetic Algorithms

Cited by 11 publications

References 5 publications

Stochastic Analysis of DSS Queries for a Distributed Database Design

Stochastic Analysis of DSS Queries for a Distributed Database Design

SQL Optimization in Oracle using Hybrid Genetic and Ant Colony Algorithm

An Efficient Approach of Block Nested Loop Algorithm Based on Rate of Block Transfer

Contact Info

Product

Resources

About