数据浪潮:数据管理和挖掘

Mohand Tahar Kechadi
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引用次数: 2

摘要

如今,大量的数据通常分布在不同的地理位置,并由不同的组织拥有。因此,大量的知识正在产生。这就导致了有效的知识管理和挖掘的问题。主要目标是开发DM基础设施,以充分利用这些非常大的数据存储库中包含的知识的好处。为此,我们引入了“知识地图”的方法来方便、高效地表示在网格等大规模平台上挖掘的知识。这也促进了当地采矿过程与现有知识的整合和协调,以提高最终模型的准确性。本文讨论了它的优点和设计问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Data Wave: Data Management and Mining
Nowadays, massive amounts of data that are often geographically distributed and owned by different organisations are being mined. As consequence, a large mount of knowledge is being produced. This causes the problem of efficient knowledge management and mining. The main aim is to develop DM infrastructures to fully exploit the benefit of the knowledge contained in these very large data repositories. To this end, we introduced ”knowledge map” approach to represent easily and efficiently the knowledge mined in a large-scale platform such as Grid. This also facilitates the integration and coordination of local mining processes along with existing knowledge to increase the accuracy of the final models. In this paper, we discuss its advantages and its design issues.
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