• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Parallel and Distributed Systems & Networks >PARALLEL MAINTENANCE OF MATERIALIZED VIEWS ON PERSONAL COMPUTER CLUSTERS
【24h】

PARALLEL MAINTENANCE OF MATERIALIZED VIEWS ON PERSONAL COMPUTER CLUSTERS

机译：并行维护个人计算机集群的材料视图

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A data warehouse is a repository of integrated information, which collects and maintains a large amount of data from multiple distributed, autonomous, and possibly heterogeneous data sources. Often the data are stored in the form of materialized views in order to provide fast access to the integrated data. How to maintain the warehouse data completely consistently with the remote source data is a challenging issue in a distributed environment. Transactions containing multiple updates at one or multiple sources further complicate this consistency issue. Due to the fact that a data warehouse usually contains a very large amount of data and its processing is time consuming, it becomes inevitable to introduce parallelism to data warehousing. The popularity and cost-effective parallelism brought by the PC cluster makes it a promising platform for this purpose. This article considers the complete consistency maintenance of select-project-join (SPJ) materialized views. Based on a PC cluster consisting of K personal computers, several parallel maintenance algorithms for the materialized views are presented. The key behind the proposed algorithms is how to trade off the work load among the PCs and how to balance the communications cost among the PCs as well as between the PC cluster and remote sources.
机译：数据仓库是集成信息的存储库，它从多个分布式，自治且可能是异构的数据源收集并维护大量数据。通常，数据以实例化视图的形式存储，以便提供对集成数据的快速访问。在分布式环境中，如何与远程源数据完全一致地维护仓库数据是一个具有挑战性的问题。包含一个或多个来源的多个更新的事务使此一致性问题进一步复杂化。由于数据仓库通常包含大量数据，并且其处理非常耗时，因此将并行性引入数据仓库变得不可避免。 PC集群带来的普及性和成本效益的并行性使其成为用于此目的的有前途的平台。本文考虑了select-project-join（SPJ）实例化视图的完全一致性维护。基于由K台个人计算机组成的PC集群，提出了针对物化视图的几种并行维护算法。提出的算法背后的关键是如何权衡PC之间的工作负载，以及如何平衡PC之间以及PC群集和远程源之间的通信成本。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号