Parallel Database Systems
Download Parallel Database Systems PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Parallel Database Systems book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Mining Very Large Databases with Parallel Processing
Author: Alex A. Freitas
language: en
Publisher: Springer Science & Business Media
Release Date: 1997-11-30
Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely `intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning.
Principles of Distributed Database Systems
Author: M. Tamer Özsu
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-02-24
This third edition of a classic textbook can be used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This book covers the breadth and depth of this re-emerging field. The coverage consists of two parts. The first part discusses the fundamental principles of distributed data management and includes distribution design, data integration, distributed query processing and optimization, distributed transaction management, and replication. The second part focuses on more advanced topics and includes discussion of parallel database systems, distributed object management, peer-to-peer data management, web data management, data stream systems, and cloud computing. New in this Edition: • New chapters, covering database replication, database integration, multidatabase query processing, peer-to-peer data management, and web data management. • Coverage of emerging topics such as data streams and cloud computing • Extensive revisions and updates based on years of class testing and feedback Ancillary teaching materials are available.
Parallel Database Systems
Author: Pierre America
language: en
Publisher: Springer Science & Business Media
Release Date: 1991-06-26
This volume presents the proceedings of a workshop on parallel database systems organized by the PRISMA (Parallel Inference and Storage Machine) project. The invited contributions by internationally recognized experts give a thorough survey of several aspects of parallel database systems. The second part of the volume gives an in-depth overview of the PRISMA system. This system is based on a parallel machine, where the individual processors each have their own local memory and communicate with each other over a packet-switched network. On this machine a parallel object-oriented programming language, POOL-X, has been implemented, which provides dedicated support for database systems as well as general facilities for parallel programming. The POOL-X system then serves as a platform for a complete relational main-memory database management system, which uses the parallelism of the machine to speed up significantly the execution of database queries. The presentation of the PRISMA system, together with the invited papers, gives a broad overview of the state of the art in parallel database systems.