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

A Genetic Programming Approach for Distributed Queries

With the emergence of relatively inexpensive and advanced communication technology, Distributed Database Management Systems (DDBMS) have become an integral part of many computer applications. Efficient query processing is one of the most important issues in distributed database systems. In a distributed environment, it is common that queries extract data from different sites. It is important to limit the amount of data transfer across different sites. Semijoin is a way to reduce the cost of expensive joins between various sites. A key issue in query optimization based on semijoin reduction is to find a good sequence of semijoins that reduce the relations referenced in a given query before the joins are performed. This paper proposes a new approach, based on Genetic Programming (GP), to improve the process of database query in Distributed Database Systems. A longer version of this paper is available

A bloom-filter strategy for response time reduction in distributed query processing.

In distributed database systems, query optimization is to find strategies attempt to minimize the amount of data transmitted over the network. Optimization algorithms have an important impact on the performance of distributed query processing. Since optimal query processing in distributed database systems has been shown to be NP-Hard [WC96], heuristics are applied to find a cost-effective and efficient (but suboptimal) processing strategy. Many query optimization strategies have been proposed to minimize either the total cost or the response time. The approaches in distributed query processing have mainly focused on the use of joins, semijoins, and filters. In this thesis, we propose a new reduction strategy based on bloom-filters to significantly reduce the response time of a distributed query. This algorithm can process general queries consisting of an arbitrary number of relations and join attributes. The performance of the algorithm with respect to response time is compared against the Initial Feasible Solution (IFS). An amount of experimental results has been used to evaluate the performance of our algorithm. Compared to the IFS, our algorithm provides a significantly improved query solution. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .G36. Source: Masters Abstracts International, Volume: 43-05, page: 1749. Thesis (M.Sc.)--University of Windsor (Canada), 2003

Distributed query optimization

The need for the distributed systems has been determined by the type of business developed by companies with offices geographically distributed where the specific organizational structure promotes a decentralized business model. This paper describes the techniques and concepts of system architecture for distributed database management systems, followed by the presentation of implementation phases involved when dealing with the distributed queries across distributed systems. The goal of query optimization is to determine the most efficient way to execute a query in a distributed environment, by obtaining a lower system response time and also by minimizing the query execution time. For this, we will analyze the factors that influence the ways to execute a query and we will also review the available strategies to optimize the distributed query execution.architecture, distributed queries, optimization, strategies.

Database Optimization Using Genetic Algorithms for Distributed Databases

Databases can store a vast amount of information and particular sets of data are accessed via queries which are written in specific interface language such as structured query language (SQL). Database optimization is a process of maximizing the speed and efficiency with which kind of data is retrieved or simply it’s a mechanism that reduces database systems response time. Query optimization is one of the major functionality in database management systems (DBMS). The purpose of the query optimization is to determine the most efficient and effective way to execute a particular query by considering several query plans such as graphical plans, textual plans and etc. Execution of any particular datasets depends on the capability of the query optimization mechanism to acquire competent query processing approaches. Distributed database system is a collection several interrelated databases which are spread physically across different environments that communicate through a computer network. Inability to obtain an effective query strategy with an efficient accuracy and minimum response time or cost to execute the given query is one of the major key issues of the query optimization in distributed database systems. Further inefficient database compression methods, inefficient query processing, missing indexes, inexact statistics, and deadlocks are furthermore defects. In this paper, it describes the methodologies such as genetic algorithm strategy for distributed database systems so as to execute the query plan. Genetic algorithms are extensively using to solve constrained and unconstrained optimization problems. The genetic algorithms are using three main types of rules such as selection rules, crossover rules, and mutation rules

Algebraic optimization of recursive queries

Over the past few years, much attention has been paid to deductive databases. They offer a logic-based interface, and allow formulation of complex recursive queries. However, they do not offer appropriate update facilities, and do not support existing applications. To overcome these problems an SQL-like interface is required besides a logic-based interface.\ud \ud In the PRISMA project we have developed a tightly-coupled distributed database, on a multiprocessor machine, with two user interfaces: SQL and PRISMAlog. Query optimization is localized in one component: the relational query optimizer. Therefore, we have defined an eXtended Relational Algebra that allows recursive query formulation and can also be used for expressing executable schedules, and we have developed algebraic optimization strategies for recursive queries. In this paper we describe an optimization strategy that rewrites regular (in the context of formal grammars) mutually recursive queries into standard Relational Algebra and transitive closure operations. We also describe how to push selections into the resulting transitive closure operations.\ud \ud The reason we focus on algebraic optimization is that, in our opinion, the new generation of advanced database systems will be built starting from existing state-of-the-art relational technology, instead of building a completely new class of systems

The role of expert systems in federated distributed multi-database systems/Ince Levent

A shared information system is a series of computer systems interconnected by some kind of communication network. There are data repositories residing on each computer. These data repositories must somehow be integrated. The purpose for using distributed and multi-database systems is to allow users to view collections of data repositories as if they were a single entity. Multidatabase systems, better known as heterogeneous multidatabase systems, are characterized by dissimilar data models, concurrency and optimization strategies and access methods. Unlike homogenous systems, the data models that compose the global database can be based on different types of data models. It is not necessary that all participant databases use the same data model. Federated distributed database systems are a special case of multidatabase systems. They are completely autonomous and do not rely on the global data dictionary to process distributed queries. Processing distributed query requests in federated databases is very difficult since there are multiple independent databases with their own rules for query optimization, deadlock detection, and concurrency. Expert systems can play a role in this type of environment by supplying a knowledge base that contains rules for data object conversion, rules for resolving naming conflicts, and rules for exchanging data.http://archive.org/details/theroleofexperts109459362Turkish Navy author.Approved for public release; distribution is unlimited

Heuristics for query optimization in distributed database systems.

The technology of distributed databases (DDB) is based on two other technologies which have developed a sufficiently solid foundation during the seventies: computer networks technology and database technology. One of the main difficulties in distributed database systems is to select an execution strategy that minimizes resource consumption. Some optimization strategies such as AHY (Apers-Hevner-Yao) Algorithms only focus on reducing the amount of transmissions. They assume that the cost to transmit the packets from one site to another site is the same. However, this is not true in the real world, since the cost of transmission is dependent on the network load situation. Therefore, it is possible to develop some heuristics which consider the network load as well. The objective of this thesis is to develop some heuristics which will take the network load into account and to compare the result with the AHY Algorithms. Source: Masters Abstracts International, Volume: 33-04, page: 1269. Advisers: Subir Bandyopadhyay; Joan Morrissey. Thesis (M.Sc.)--University of Windsor (Canada), 1994

Evaluation of Optimization Strategies for Incremental Graph Queries

The last decade brought considerable improvements in distributed storage and query technologies, known as NoSQL systems. These systems provide quick evaluation of simple retrieval operations and are able to answer certain complex queries in a scalable way, albeit not instantly. Providing scalability and quick response times at the same time for querying large data sets is still a challenging task. Evaluating complex graph queries is particularly difficult, as it requires lots of join, antijoin and filtering operations. This paper presents optimization techniques used in relational database systems and applies them on graph queries. We evaluate various query plans on multiple datasets and discuss the effect of different optimization techniques