Global schema as local data integrator using active learning to identify candidates attributes

Clóvis Santos, Carina Dorneles
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Abstract

Data integration represents a challenge in application development. Although there are several alternatives to data integration, such as federated and distributed databases, there are still problems with the standardisation of distinct data sources, and this happens because different companies develop distinct systems with different paradigms and concepts. In this paper, we present a case study, in the agriculture and environment domain, of an essential point in the data integration domain which is to show resources to identify nearby attributes concerning the characteristics of the content foreseen in the requirements presented in the proposed schema. Information technology experts in agribusiness help map the most relevant attributes for the investigated scenario. In our experimental tests, we used a quantitative method data analysis approach to validate the results with quantitative comparisons regarding the percentages of proximity between the attribute contents in the databases. Our proposal presents an alternative to simplify data integration without intermediate application or middleware layers. The results were measured on a scale between 0% and 100% to identify candidate attributes. The results were good in identifying attributes in the databases in almost 67% of the cases.
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全局模式作为本地数据集成商,使用主动学习来识别候选属性
数据集成是应用程序开发中的一个挑战。尽管有几种数据集成的替代方案,比如联邦数据库和分布式数据库,但是不同数据源的标准化仍然存在问题,这是因为不同的公司使用不同的范例和概念开发不同的系统。在本文中,我们提出了一个农业和环境领域的案例研究,该案例研究了数据集成领域中的一个关键点,即显示资源以识别与所提出模式中所提出的需求中所预见的内容的特征相关的附近属性。农业综合企业的信息技术专家帮助为所调查的场景绘制最相关的属性。在我们的实验测试中,我们使用定量方法数据分析方法,通过对数据库中属性内容之间的接近百分比进行定量比较来验证结果。我们的建议提供了一种替代方案,可以在没有中间应用程序或中间件层的情况下简化数据集成。结果在0%到100%之间进行测量,以确定候选属性。在几乎67%的情况下,结果很好地识别了数据库中的属性。
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来源期刊
International Journal of Applied Management Science
International Journal of Applied Management Science Business, Management and Accounting-Strategy and Management
CiteScore
1.20
自引率
0.00%
发文量
21
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