Pattern based processing of XPath queries

As the popularity of areas including document storage and distributed systems continues to grow, the demand for high performance XML databases is increasingly evident. This has led to a number of research eorts aimed at exploiting the maturity of relational database systems in order to in- crease XML query performance. In our approach, we use an index structure based on a metamodel for XML databases combined with relational database technology to facilitate fast access to XML document elements. The query process involves transforming XPath expressions to SQL which can be executed over our optimised query engine. As there are many dierent types of XPath queries, varying processing logic may be applied to boost performance not only to indi- vidual XPath axes, but across multiple axes simultaneously. This paper describes a pattern based approach to XPath query processing, which permits the execution of a group of XPath location steps in parallel

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

Schema architecture and their relationships to transaction processing in distributed database systems

We discuss the different types of schema architectures which could be supported by distributed database systems, making a clear distinction between logical, physical, and federated distribution. We elaborate on the additional mapping information required in architecture based on logical distribution in order to support retrieval as well as update operations. We illustrate the problems in schema integration and data integration in multidatabase systems and discuss their impact on query processing. Finally, we discuss different issues relevant to the cooperation (or noncooperation) of local database systems in a heterogeneous multidatabase system and their relationship to the schema architecture and transaction processing

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

Towards application-specific query processing systems

Database systems use query processing subsystems for enabling efficient query-based data retrieval. An essential aspect of designing any query-intensive application is tuning the query system to fit the application's requirements and workload characteristics. However, the configuration parameters provided by traditional database systems do not cover the design decisions and trade-offs that arise from the geo-distribution of users and data. In this paper, we present a vision towards a new type of query system architecture that addresses this challenge by enabling query systems to be designed and deployed in a per use case basis. We propose a distributed abstraction called Query Processing Unit that encapsulates primitive query processing tasks, and show how it can be used as a building block for assembling query systems. Using this approach, application architects can construct query systems specialized to their use cases, by controlling the query system's architecture and the placement of its state. We demonstrate the expressiveness of this approach by applying it to the design of a query system that can flexibly place its state in the data center or at the edge, and show that state placement decisions affect the trade-off between query response time and query result freshness

SAP HANA distributed in-memory database system: Transaction, session, and metadata management

One of the core principles of the SAP HANA database system is the comprehensive support of distributed query facility. Supporting scale-out scenarios was one of the major design principles of the system from the very beginning. Within this paper, we first give an overview of the overall functionality with respect to data allocation, metadata caching and query routing. We then dive into some level of detail for specific topics and explain features and methods not common in traditional disk-based database systems. In summary, the paper provides a comprehensive overview of distributed query processing in SAP HANA database to achieve scalability to handle large databases and heterogeneous types of workloads

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

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