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

Golshanara, Ladan; Rankoohi, Seyed Mohammad Rouhani; Shah‐Hosseini, Hamed

doi:10.1007/s10115-012-0608-4

Cited by 23 publications

(20 citation statements)

References 32 publications

(59 reference statements)

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“…In order to measure the effectiveness of the global query execution plans, we develop a cost model that is based on the total execution time of the queries [35,24,36]. The model uses QP trees that can be executed in a parallel manner by several computers to examine every factor in detail [44].…”

Section: Cost Modelmentioning

confidence: 99%

Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries

Dökeroğlu

Bayır

Çoşar

2015

Applied Soft Computing

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Section: Cost Modelmentioning

confidence: 99%

Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries

Dökeroğlu

Bayır

Çoşar

2015

Applied Soft Computing

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“…The problem of query optimization that involves set operations to achieve minimum communication costs in a distributed database system is NP-hard problem. 8,39 For a complex query (involving many relations and many operators), the number of equivalent operator trees can be very high. 40 The answers to a user query are provided by the combination of relations.…”

Section: Problem Statementmentioning

confidence: 99%

“…42 In the distributed databases, data may be replicated on different sites over the shared network. 8,40 Join query in distributed databases is used to join the data from multiple sites. Each combination can be a query plan.…”

Section: Problem Statementmentioning

confidence: 99%

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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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“…Golshanara et al 65 have proposed an Ant Colony Optimizer (ACO) to order the query plans to join operations in an environment with the data replicated across multiple sites. In the proposed algorithm, four types of ants collaborate with each other to provide an efficient execution plan and each colony makes a decision to find the optimal plan.…”

Section: An Overview Of Non-deterministic Cost-based Query Plan Enumementioning

confidence: 99%

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Azhir

Navimipour

Hosseinzadeh

et al. 2019

Concurrency and Computation

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Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP-hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non-deterministic methods are two main categories to study these techniques. The deterministic and non-deterministic query optimization methods can be further divided into three subcategories, cost-based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future. KEYWORDScloud computing, database, query optimization, review INTRODUCTIONThe data transfer operation and resource sharing are facilitated by rapid progress of the distributed IT-based systems. 1,2 Cloud computing supports several computers through a network. 3 The cloud computing has a large-scale distributed architecture and virtualized services to deliver the requests to users. 4,5 Moreover, the cloud computing provides important financial advantages and long level cooperation possibilities for organizations and institutions. 6 The cloud computing is defined as a distributed IT-based technology based on service business model. 7This paradigm provides many benefits for users, such as the provision of computing capabilities, heterogeneous network access, scalability, and elasticity with measured services. 8,9 The cloud computing gives shared access to a large pool of resources, including data storage, memory, processing, and virtual machines. 10 A cloud client, such as a web browser and mobile app can be helpful in accessing these services. 11 Enormous amounts of data are retrieved from geo-distributed data sources and cross-layer data-handling requirements to make a change in business model. 12 The cloud storage as one of the main services is provided by cloud computing, 13 which allows the users to store their data in virtual pools instead of their servers. 14 In addition, subscribers can access the data from any area of cloud. 15 Therefore, the reliability and availability are necessary to recover the information and query processing.The query processing involves three main steps, as shown in Figure 1. First, the query is translated into an expression of the relational algebra.Second, an optimal evaluation plan for the query plan is generated. The query optimization is the main part o...

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

Cited by 23 publications

References 32 publications

Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries

Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries

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

Deterministic and non‐deterministic query optimization techniques in the cloud computing

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