Distributed Database Query Optimization Research Articles

Optimization Method for Distributed Database Query Based on an Adaptive Double Entropy Genetic Algorithm

In a distributed database environment, multi-join query optimization is one of the key factors affecting database performance. Genetic algorithms have a good application in dealing with this type of problem. However, the traditional genetic algorithm has the problems of low efficiency and easily falls into the precocity when dealing with query optimization, which is mainly caused by the lack of population diversity. Therefore, this paper sets up a mathematical model for distributed database query optimization and proposes an adaptive genetic algorithm based on double entropy. We introduced a genetic algorithm with two types of entropy: genotype and phenotype. Genotype entropy was used to optimize the distribution of the initial population, ensuring that the initial population has good population diversity. Phenotype entropy is used to optimize the genetic strategy, which can be divided into individual entropy and population entropy. Individual entropy is used to optimize the selection strategy, and population entropy is used to optimize the crossover and mutation operators to maintain the population diversity in the iteration process and accelerate the speed of iteration. The experimental results show that the algorithm proposed in this paper is effective for query optimization of a distributed database.

Optimization of correlate subquery based on distributed database

Subquery is widely used in database. It can be divided into related subquery and non-related subquery according to whether it is dependent on the table of the parent query. For related subqueries, it is necessary to take a tuple from the parent query before executing the subquery, that is, the content of the subquery needs to be repeatedly operated. Disk access costs of this strategy is very big, in the distributed database, because of data communication overhead, in the parent query yuan set is too low efficiency, therefore, for the class sub queries, on the basis of the optimization of the existing query strategy, combining with the characteristics of distributed database, put forward by the subquery on to join queries, eliminate redundant clauses in the subquery, eliminate accumulation function method based on distributed database query optimization strategy, and the effectiveness of the present optimization strategy is verified by experiment.

Assessment of Urban Water Supply System Based on Query Optimization Strategy

As a complicated water treatment system project, an urban water supply plays a significant role in enhancing ecological civilization construction, promoting social-economic sustainable development, and improving the living environment of humans. This paper has the goal of improving water treatment efficiency and reducing water treatment cost based on comparative studies by applying two types of distributed database query optimization methods, including the system for a distributed database (SDD-1) and all reduction algorithms. The approach involves the components of partial relations and deletes the database components that are unrelated to a query, using only attributes related to a query. Thereby, the realization process becomes simple, with a small amount of transmitted data. The results show that the validity and convenience of all reduction algorithms involve the whole query process. An algorithm is independent of the chart of static characteristics and can realize all reduction states without depending on analyzing the benefits from intermediate semijoin results, which will ultimately contribute to reductions in the transmission of useless data in the network and communication costs. Implementation of the query optimization strategy can improve water treatment efficiency, reduce water treatment cost, lower water treatment expense, and implement effective communication.

Evolutionary Algorithms for Query Op-timization in Distributed Database Sys-tems: A review

Evolutionary Algorithms are bio-inspired optimization problem-solving approaches that exploit principles of biological evolution. , such as natural selection and genetic inheritance. This review paper provides the application of evolutionary and swarms intelligence based query optimization strategies in Distributed Database Systems. The query optimization in a distributed environment is challenging task and hard problem. However, Evolutionary approaches are promising for the optimization problems. The problem of query optimization in a distributed database environment is one of the complex problems. There are several techniques which exist and are being used for query optimization in a distributed database. The intention of this research is to focus on how bio-inspired computational algorithms are used in a distributed database environment for query optimization. This paper provides working of bio-inspired computational algorithms in distributed database query optimization which includes genetic algorithms, ant colony algorithm, particle swarm optimization and Memetic Algorithms.

Static vs Dynamic Techniques for Selectivity Evaluation in Distributed Query Optimization

In distributed database query optimization, query optimizers have traditionally relied upon statically estimated table cardinalities when evaluating the cost of the query plans. This paper analyses static vs. dynamic calculation for selectivity of intermediate relations generated in query processing. The objective of this research is to overcome the disadvantages of previously formulated static methods which are relatively inaccurate in a distributed database environment. A Dynamic selectivity evaluation tool (DSET) has been proposed to optimize cost for a distributed database query processing environment. The results have shown that dynamic evaluation of selectivity factor of sub query operation is feasible and can significantly reduced the total query cost than its static estimation.

Study on Distributed Database Query Optimization

This paper presents a heterogeneous sensor networks to improve query processing mechanism. Analysis of the advantages and disadvantages of centralized query processing algorithm is proposed based on the spatial distance distributed query processing algorithm based on semantic similarity and distributed query processing algorithms for query execution processes described. Use simulation to choose better coverage and semantic similarity by half, and centralized query processing algorithm, based on the spatial distance distributed algorithms, distributed algorithms and performance-based semantic distributed hybrid algorithm were compared.

A New Recombination Operator for the Genetic Algorithm Solution of the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is a well known combinatorial optimization problem with a diverse set of applications. It can be transformed into many problems such as the travelling salesman, weapon target assignment, and query optimization in distributed databases. Exhaustive search methods are inadequate to solve large data sets. Genetic algorithms and tabu search meta-heuristics may provide near optimal solutions for large QAP instances taking a reasonable time to complete. In this paper, we present a new recombination operator based on Order-1 crossover algorithm. The suggested approach runs quick sort partitioning algorithm to generate different chromosomes from partitions. The minimum cost partition produces offsprings with the other chromosome. The proposed approach shows outstanding performance especially for instance sizes smaller than 50 with respect to the optimal results proposed in QAPLIB.

Research on Distributed Database Query Optimization Based on Genetic Algorithm

In order to improve the performance of the query optimization for the distribute database, an improved query optimization algorithm was proposed based on the genetic algorithm. The query execution cost model based on the genetic algorithm was proposed in this paper. The distributed database was emerged in the 70's of the last century and developed with the progress of the computer technology and network technology, the distributed database was the database system which is distributed storage dispersedly in physics and with centralized processing in mathematic logic. Because the storage points were not uniform, the structure of the distributed database is much more complicated than the centralized database. Both the genetic algorithm and the dynamic exhaustive planning algorithm were taken in the query simulation for the performance comparison. The result shows that the genetic query optimization method in this paper has better performance in the distributed database query application. The case study and the simulation result show that the algorithm can get a satisfactory optimization result in a few iterations and the query optimization algorithm based on the genetic method has nice performance of the query optimization property, and the consumption and costs of the query is reduced to the minimum. The method which this paper proposed has good application performance and is valuable to put into practice.

Novel Distributed Query Optimization Model and Hybrid Query Optimization Algorithm

Query optimization is the most critical phase in query processing. Query optimization in distributed databases explicitly needed in many aspects of the optimization process, this is not only increases the cost of optimization, but also changes the trade-offs involved in the optimization process significantly .This paper describes the synthetically evolution of query optimization methods from uniprocessor relational database systems to parallel database systems. We point out a set of parameters to characterize and compare query optimization methods, mainly: (i) type of algorithm (static or dynamic), (ii) working environments (re-optimization or re-scheduling) and (iii) level of modification. The major contributions of this paper are: (I) Understanding the mechanisms of query optimization methods with respect to the considered environments and their constraints (e.g. parallelism, distribution, heterogeneity, large scale, dynamicity of nodes). (ii) Study the problem of query optimization particular in term of heterogeneously environment and pointing out their main characteristics, which allow comparing them and help to Implement new query optimization algorithm and model. These contributions is led to performance enhancement of query optimization in distributed database system through classify by different QEPs and minimize the response time.

Analysis of Query Optimization Techniques in Databases

Abdullah Dilsat : Query Optimization in Distributed Databases. Report, Middle East Technical University, December 2003. Aho, A. V. , Sagiv,Y. and J. D. Ullman: Efficient optimization of a class of relational expressions. ACM Trans. Database Systems. 4, 4, p435-454, 1979. Chaudhuri S. and K. Shim. Query optimization with aggregate views. In Proceedings of the 5th International Conference on Extending Database Technology, Avignon, France, March 1996.

Research of Query Optimization Based on Improved Quantum Particle Swarm Optimization Algorithm in Distributed Database

Database query optimization is a very complicated issue, also is the key influencing factor in database systems performance. Database query operation efficiency is one of the key factors that affect system response time. Therefore, how to improve the efficiency of database query system becomes particularly important. This paper, on the basis of the advantages of Quantum particle swarm optimization algorithm, proposes distributed database query optimization methods based on Quantum particle swarm optimization algorithm, and improves algorithm. Simulation comparison experiments show that Quantum particle swarm optimization algorithm can improve the efficiency of the distributed database query, and is an effective way to solve the optimization of distributed database query.

Stochastic query optimization in distributed databases

Many algorithms have been devised for minimizing the costs associated with obtaining the answer to a single, isolated query in a distributed database system. However, if more than one query may be processed by the system at the same time and if the arrival times of the queries are unknown, the determination of optimal query-processing strategies becomes a stochastic optimization problem. In order to cope with such problems, a theoretical state-transition model is presented that treats the system as one operating under a stochastic load. Query-processing strategies may then be distributed over the processors of a network as probability distributions, in a manner which accommodates many queries over time. It is then shown that the model leads to the determination of optimal query-processing strategies as the solution of mathematical programming problems, and analytical results for several examples are presented. Furthermore, a divide-and-conquer approach is introduced for decomposing stochastic query optimization problems into distinct subproblems for processing queries sequentially and in parallel.

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

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.

Query optimization in distributed databases through load balancing

Abstract : As technology advances, computing environments composed of large numbers of workstations and mainframes connected by a high bandwidth local area network become attractive. These systems typically support a large number of application classes, and the heterogeneity of their workload, coupled with the decentralization of the systems, can lead to load imbalances across the network. This work attempts to study the benefits of load balancing strategies in the context of a particular application, distributed database systems. It was felt that, by focusing on a specific area, the problem would become more tractable. The choice of database management systems can be justified not only by their intrinsic importance, but also by the adaptability of load balancing strategies to query optimization algorithms. In order to determine whether load balancing strategies can indeed be adapted to current optimizers with a moderate amount of effort and to see whether the resulting performance benefits are sizable, both benchmarking and simulation experiments were carried out.

Query Optimization in Database Systems

Efficient methods of processing unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches to improve the performance of query evaluation algorithms have been proposed: logic-based and semantic transformations, fast implementations of basic operations, and combinatorial or heuristic algorithms for generating alternative access plans and choosing among them. These methods are presented in the framework of a general query evaluation procedure using the relational calculus representation of queries. In addition, nonstandard query optimization issues such as higher level query evaluation, query optimization in distributed databases, and use of database machines are addressed. The focus, however, is on query optimization in centralized database systems.