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Data mining and linked open data - New perspectives for data analysis in environmental research

机译:数据挖掘和链接的开放数据-环境研究中数据分析的新视角

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The rapid development in information and computer technology has facilitated an extreme increase in the collection and storage of digital data. However, the associated rapid increase in digital data volumes does not automatically correlate with new insights and advances in our understanding of those data. The relatively new technique of data mining offers a promising way to extract knowledge and patterns from large, multidimensional and complex data sets. This paper therefore aims to provide a comprehensive overview of existing data mining techniques and related tools and to illustrate the potential of data mining for different research areas by means of example applications. Despite a number of conventional data mining techniques and methods, these classical approaches are restricted to isolated or "silo" data sets and therefore remain primarily stand alone and specialized in nature. Highly complex and mostly interdisciplinary questions in environmental research cannot be answered sufficiently using isolated or area-based data mining approaches. To this end, the linked open data (LOD) approach will be presented as a new possibility in support of complex and inter-disciplinary data mining analysis. The merit of LOD will be explained using examples from medicine and environmental research. The advantages of LOD data mining will be weighed against classical data mining techniques. LOD offers unique and new possibilities for interdisciplinary data analysis, modeling and projection for multidimensional, complex landscapes and may facilitate new insights and answers to complex environmental questions. Our paper aims to encourage those research scientists which do not have extensive programming and data mining knowledge to take advantage of existing data mining tools, to embrace classical data mining and LOD approaches in support of gaining more insight and recognizing patterns in highly complex data sets. (C) 2014 Elsevier B.V. All rights reserved.
机译:信息和计算机技术的飞速发展促进了数字数据的收集和存储的极大增加。但是,相关的数字数据量的快速增长并不会自动与新的见解和我们对这些数据的理解的进步相关联。相对较新的数据挖掘技术提供了一种从大型,多维和复杂数据集中提取知识和模式的有前途的方法。因此,本文旨在提供对现有数据挖掘技术和相关工具的全面概述,并通过示例应用程序来说明不同研究领域的数据挖掘潜力。尽管有许多常规的数据挖掘技术和方法,但是这些经典的方法仅限于隔离的或“独立的”数据集,因此本质上仍然保持独立和专门化。使用孤立的或基于区域的数据挖掘方法无法充分回答环境研究中高度复杂且跨学科的问题。为此,链接开放数据(LOD)方法将作为支持复杂且跨学科的数据挖掘分析的一种新可能性而提出。将使用医学和环境研究中的示例来说明LOD的优点。 LOD数据挖掘的优势将与传统数据挖掘技术权衡。 LOD为多维,复杂景观的跨学科数据分析,建模和投影提供了独特而又新的可能性,并且可以促进对复杂环境问题的新见解和答案。本文旨在鼓励那些没有广泛编程和数据挖掘知识的研究科学家利用现有的数据挖掘工具,拥抱经典的数据挖掘和LOD方法,以支持在高度复杂的数据集中获得更多的见识和模式识别。 (C)2014 Elsevier B.V.保留所有权利。

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