Multi-Join Query Optimization Using the Bees Algorithm

Alamery, Mohammad; Faraahi, Ahmad; Javadi, Hamid Haj Seyyed; Nourossana, Sadegh; Erfani, Hossein

doi:10.1007/978-3-642-14883-5_58

Cited by 12 publications

(9 citation statements)

References 6 publications

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“…However, these algorithms are successfully applied in centralized query optimization [1,21] and distributed database design [13,18]. Our proposed algorithm employs the model once introduced in OGA97 algorithm and its search strategy is based on the ant colony optimization metaheuristic.…”

Section: Related Workmentioning

confidence: 99%

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A multi-colony ant algorithm for optimizing join queries in distributed database systems

2013

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Distributed database systems provide a new data processing and storage technology for decentralized organizations of today. Query optimization, the process to generate an optimal execution plan for the posed query, is more challenging in such systems due to the huge search space of alternative plans incurred by distribution. As finding an optimal execution plan is computationally intractable, using stochastic-based algorithms has drawn the attention of most researchers. In this paper, for the first time, a multi-colony ant algorithm is proposed for optimizing join queries in a distributed environment where relations can be replicated but not fragmented. In the proposed algorithm, four types of ants collaborate to create an execution plan. Hence, there are four ant colonies in each iteration. Each type of ant makes an important decision to find the optimal plan. In order to evaluate the quality of the generated plan, two cost models are used-one based on the total time and the other on the response time. The proposed algorithm is compared with two previous genetic-based algorithms on chain, tree and cyclic queries. The experimental results show that the proposed algorithm saves up to about 80 % of optimization time with no significant difference in the quality of generated plans compared with the best existing genetic-based algorithm.

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Section: Related Workmentioning

confidence: 99%

“…fragmentation and allocation) in [13,18]. Furthermore, ant and bee colony algorithms have been applied for join query optimization in centralized database systems which have led to better results compared to genetic algorithms [1,21]. Two cost models are introduced-one based on the total time, and the other on the response time.…”

Section: Introductionmentioning

confidence: 98%

A multi-colony ant algorithm for optimizing join queries in distributed database systems

2013

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“…Shan et al [22] presented an optimization scheme of parallel execution for multi-join queries. Recently, Alamery et al [1] devised a multi-join optimization scheme using the bees algorithm.…”

Section: Multi-query(join) Optimization Techniquesmentioning

confidence: 99%

Efficient batch similarity join processing of social images based on arbitrary features

Zhuang

Jiang

et al. 2015

World Wide Web

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In this paper, we identify and solve a multi-join optimization problem for Arbitrary Feature-based social image Similarity JOINs(AFS-JOIN). Given two collections(i.e., R and S) of social images that carry both visual, spatial and textual(i.e., tag) information, the multiple joins based on arbitrary features retrieves the pairs of images that are visually, textually similar or spatially close from different users. To address this problem, in this paper, we have proposed three methods to facilitate the multi-join processing: 1) two baseline approaches(i.e., a naïve join approach and a maximal threshold(MT)-based), and 2) a Batch Similarity Join(BSJ) method. For the BSJ method, given m users' join requests, they are first conversed and grouped into m″ clusters which correspond to m″ join boxes, where m > m″. To speedup the BSJ processing, a feature distance space is first partitioned into some cubes based on four segmentation schemes; the image pairs falling in the cubes are indexed by the cube tree index; thus BSJ processing is transformed into the searching of the image pairs falling in some affected cubes for m″ AFS-JOINs with the aid of the index. An extensive experimental evaluation using real and synthetic datasets shows that our proposed BSJ technique outperforms the state-of-the-art solutions.

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“…26 Like other population-based algorithms, the ABC algorithm has some disadvantages. 28,29 Therefore, in this paper, an ABC-GO is developed for optimizing join queries in a distributed environment by overcoming the disadvantage of these algorithms. 28,29 Therefore, in this paper, an ABC-GO is developed for optimizing join queries in a distributed environment by overcoming the disadvantage of these algorithms.…”

mentioning

confidence: 99%

“…27 The slower convergence speed and easily get trapped in local optima for some complex multimodal problem are the disadvantages. 28,29 Therefore, in this paper, an ABC-GO is developed for optimizing join queries in a distributed environment by overcoming the disadvantage of these algorithms. In a nutshell, the main contributions of this paper are as follows:…”

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confidence: 99%

Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

Panahi

Navimipour

2019

Concurrency and Computation

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As the main factor in the distributed database systems, query optimization is aimed at finding an optimal execution plan to reduce the runtime. In such systems, because of the repeated relations on various sites, the query optimization is very challenging. Moreover, the query optimization issue with large-scale distributed databases is an NP-hard problem. Therefore, in this paper, an Artificial Bee Colony Algorithm based on Genetic Operators (ABC-GO) is proposed to find a solution to join the query optimization problems in the distributed database systems. The ABC algorithm has the global-local search capabilities and genetic operators to create new candidate solutions for improving the performance of the ABC algorithm. The obtained results have shown that the cost of the query evaluation is minimized and the quality of Top-K query plans is improved for a given distributed query. Moreover, this method decreases the overhead.However, it needs a longer execution time. KEYWORDSartificial bee colony optimization, distributed database, genetic operator, query plan optimization INTRODUCTIONDue to the rapid growth of data in current systems, 1,2 a set of the interrelated databases, known as a distributed database, is stored on multiple computers to improve performance, reliability, accessibility, and modularity in such cases compared to the conventional centralized database system. 3-7 In today's world, every user of the system, whether an employee or a customer, needs access to the company's databases. The data in distributed databases may be repeated at different sites according to a distributed allocation plan. 8-10 As a result, an association among different sites is needed to create answers to a user query. 11 The query optimization is a critical subject for the distributed database management system. Therefore, the poor performance of database for answering the query is the result of poor selection of a query execution plan. 12The query is a statement or a group of statements that performs some simple database operations such as deletion, updating, and retrieving data. It plays a critical role in data management and retrieval. Commonly, the distributed queries are more complicated than centralized queries due to data distribution over different sites. 13 Consequently, the impact of efficient query processing is increasing in a large number of applications.A common method for identifying the Top-k objects is that all database objects are scored on some function. Therefore, the DQP (distributed query process) strategy aims to generate the query processing plans that reduce the amount of data transmission between sites and, as a result, reduces the response time of the distributed query. The query optimization is a critical subject for database management systems, 14 whose purpose is to define an appropriate execution plan for the user's query. 15 The cost of each plan should be produced due to more than one plan for such a query, which is significantly dependent on the amount of participation and data transfer betwe...

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Multi-Join Query Optimization Using the Bees Algorithm

Cited by 12 publications

References 6 publications

A multi-colony ant algorithm for optimizing join queries in distributed database systems

A multi-colony ant algorithm for optimizing join queries in distributed database systems

Efficient batch similarity join processing of social images based on arbitrary features

Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators

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