商业智能:属性和特征需求

Gerald V. Post, A. Kagan
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引用次数: 3

摘要

数据挖掘和商业智能工具一直在添加功能并获得用途,而为数据挖掘任务开发的统计工具通常需要高级知识和培训才能应用。这些选定工具的开发需要在易用性和功率方面进行权衡。本研究要求用户评估各种工具和属性,以确定各种组件的相对价值,并为改进和新工具提供方向。评估多属性软件是一项具有挑战性的任务,本研究提供了一种评估数据和分析权衡的方法。该过程采用结构方程模型(SEM)。评估的每个现有工具都有不同的相对优势,因此将组织的主要任务与工具的相对优势相匹配是很重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Business Intelligence: Attribute and Feature Demand
Data mining and business intelligence tools have been adding features and gaining uses, and statistical tools developed for data mining tasks often require advanced knowledge and training to apply. Development of these selected tools requires tradeoffs in ease of use and power. This study asks users to evaluate the various tools and attributes to identify the relative value of the various components and provide direction for improvements and new tools. Evaluating multi-attribute software is a challenging task, and this study provides a method of evaluating the data and analyzing tradeoffs. A structured equation model (SEM) is applied to the process. Each of the existing tools evaluated have different relative strengths, so it is important to match the organization’s primary tasks to the relative strengths of the tool.
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