科学数据库中的数据挖掘与建模

E. Kapetanios, M. Norrie
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引用次数: 0

摘要

在过去的几十年里,各种旨在更全面地了解环境的科学实验的执行,显示出数据生产的巨大增长。数据库模型为将真实世界的应用程序映射到计算机绑定的现实提供了一种或多或少适当的机制。由于科学知识只能在一定程度上被先验地建模,因此问题就出现了,数据库模式如何能够进化。另一方面,知识可以由应用数据挖掘算法的底层科学数据提供。出现的主要问题是如何提供一个合适的环境来容纳来自数据分析任务的结果,以及数据库模型如何支持这些任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data mining and modeling in scientific databases
In the last few decades, the execution of various scientific experiments aimed at a more comprehensive understanding of one's environment, has shown a tremendous increase in data production. Database models provide a more or less adequate mechanism for mapping real-world applications into a computer-bound reality. Since scientific knowledge can be modelled a priori only to some extent, the question arises of how able a database schema is to evolve. On the other hand, knowledge can be provided by the underlying scientific data on which data mining algorithms are applied. The main question which arises is how to provide a suitable environment in order to accommodate the results coming out from data analysis tasks and how these tasks can be supported by a database model.
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