大数据:对新兴数据网络的挑战

J. Hendler
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引用次数: 2

摘要

“大数据”通常指的是科学家生成的非常大的数据集,指的是Facebook和谷歌等公司持有的数拍字节的数据,以及分析实时数据资产,比如来自世界各地事件的twitter消息流。感兴趣的关键领域包括管理更大数据集的技术、数据库的可视化和分析技术、基于云的数据管理和数据挖掘算法。然而,最近,我们开始看到另一个同样引人注目的数据挑战的出现——“广泛数据”,它来自万维网上数以百万计的原始数据集。对于广泛的数据,出现的新挑战包括web规模的数据搜索和发现、数据集的快速和潜在的特别集成、仅部分建模的数据集的可视化和分析,以及与数据使用、重用和组合策略相关的问题。在这次演讲中,我们提出了广泛的数据挑战,并讨论了解决方案的潜在起点,包括语义网领域的研究。我们使用来自“元目录”的数据来说明这些方法,这些数据来自世界各地大约200个政府收集的超过1,000,000个开放数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Broad Data: Challenges on the emerging Web of data
"Big Data" usually refers to the very large datasets generated by scientists, to the many petabytes of data held by companies like Facebook and Google, and to analyzing real-time data assets like the stream of twitter messages emerging from events around the world. Key areas of interest include technologies to manage much larger datasets, technologies for the visualization and analysis of databases, cloud-based data management and data mining algorithms. Recently, however, we have begun to see the emergence of another, and equally compelling data challenge -- that of the "Broad data" that emerges from millions and millions of raw datasets available on the World Wide Web. For broad data the new challenges that emerge include Web-scale data search and discovery, rapid and potentially ad hoc integration of datasets, visualization and analysis of only-partially modeled datasets, and issues relating to the policies for data use, reuse and combination. In this talk, we present the broad data challenge and discuss potential starting points for solutions including those arising from research in the Semantic Web area. We illustrate these approaches using data from a "meta-catalog" of over 1,000,000 open datasets that have been collected from about two hundred governments around the world.
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