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An intelligent approach to improve the performance of a data warehouse cache based on association rules

机译:一种基于关联规则提高数据仓库缓存性能的智能方法

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摘要

In the world of business applications, it is significantly important to reflect patterns and trends of customers, all this to make tactical and strategic decisions. The data warehouse holds information management and turns it into meaningful management information, from which, very interesting patterns can be discovered by applying knowledge discovery process. The use of Online analytical processing with other related technologies such as data mining, can meet the needs related to business management analysis of an organization. Most analytical activities are completed remotely, and because of the huge data size of the Data Warehouse systems. So we need tools that strengthen applications to access the requested information quickly. As the update of the Data Warehouse is not too frequent, it is possible to improve query performance while storing the data retrieved by them in a cache. However, the most powerful systems have a small capacity to store the entire database in memory cache. The caching chunks technique is designed to keep in cache the query results in the form of chunks of values, instead of storing them in large tables. In this paper, we propose a new technique for caching multidimensional queries based on association rules. Using this technique will allow all users to enjoy the benefits of Data Warehousing in the best manner, and also to improve performance and also increase the use of the system while reducing the response time. The technique is build using an architecture comprising a data warehouse, a memory cache on the server and a one on each user's machine, in which the association rules and query results are stored. These results are kept in the form of chunks to enjoy all the advantages of the technique of fragmentation into chunks. This approach has been implemented and tested over a real huge data followed by displaying the results and analyzes.
机译:在业务应用程序世界中,反映客户的模式和趋势非常重要,这一切都需要制定战术和战略决策。数据仓库负责信息管理,并将其转变为有意义的管理信息,通过应用知识发现过程,可以发现非常有趣的模式。在线分析处理与其他相关技术(例如数据挖掘)的结合使用可以满足与组织的业务管理分析相关的需求。由于数据仓库系统的巨大数据量,大多数分析活动都是远程完成的。因此,我们需要能够增强应用程序以快速访问所需信息的工具。由于数据仓库的更新不太频繁,因此可以提高查询性能,同时将它们检索到的数据存储在缓存中。但是,功能最强大的系统的容量很小,无法将整个数据库存储在内存缓存中。缓存块技术旨在将查询结果以值块的形式保留在缓存中,而不是将它们存储在大表中。在本文中,我们提出了一种基于关联规则缓存多维查询的新技术。使用此技术将使所有用户以最佳方式享受数据仓库的好处,并提高性能并增加系统的使用率,同时减少响应时间。该技术是使用一种体系结构构建的,该体系结构包括数据仓库,服务器上的内存缓存以及每个用户计算机上的一个内存,其中存储了关联规则和查询结果。这些结果以块的形式保存,以享受碎片化技术的所有优点。此方法已在大量真实数据上实施和测试,然后显示结果和分析。

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