使用客户机/服务器系统进行数据挖掘

Q2 Computer Science Journal of Systems and Information Technology Pub Date : 2000-12-01 DOI:10.1108/13287260080000756

P. Pendharkar, J. Rodger

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引用次数: 2

摘要

客户机/服务器(C/S)系统已经彻底改变了系统开发方法。与基于大型机的事务处理系统相比，C/S系统的驱动因素之一是其较低的性价比。数据挖掘是在公司事务和操作数据库(也称为数据仓库)中识别模式的过程。由于大多数财富500强公司正在迅速向客户机/服务器系统转移，因此使数据挖掘方法适用于C/S系统变得越来越重要。在本文中，我们描述了C/S系统中使用的不同数据挖掘方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Data mining using client/server systems

client/server(C/S) systems have revolutionized the systems development approach. Among the drivers of the C/S systems is the lower price/performance ratio compared to the mainframe‐based transaction processing systems. Data mining is a process of identifying patterns in corporate transactional and operational databases (also called data warehouses). As most Fortune 500 companies are moving quickly towards the client server systems, it is increasingly becoming important that a data mining approaches should be adapted for C/S systems. In the current paper, we describe different data mining approaches that are used in the C/S systems.

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来源期刊

Journal of Systems and Information Technology Computer Science-Computer Science (all)

CiteScore

4.40

自引率

0.00%

发文量

期刊介绍： The Journal provides an avenue for scholarly work that researches systems thinking applications, information systems, electronic business, data analytics, information sciences, information management, business intelligence, and complex adaptive systems in the application domains of the business environment, health, the built environment, cultural settings, and the natural environment. Papers examine the wider implications of the systems or technology being researched. This means papers consider aspects such as social and organisational relevance, business value, cognitive implications, social implications, impact on individuals or community perspectives, and the development of solutions, rather than focusing solely on the technology. The Journal of Systems and Information Technology is open to a wide range of research methodologies and paper styles including case studies, surveys, experiments, review papers, design science, design thinking and both theoretical and methodological papers. The focus of the journal will be to publish work that fits into the following broad areas of research: Behavioural Information Systems and Human-Computer Interaction, Data Analytics, Data, Information and Security, E-Business, Intelligent Systems and Applications, Logistics and Supply Chain Management/Optimisation, Social Media Analysis, Technology Enhanced Learning.