历时关联数据:面向结构化关联信息的长期保存

S. Auer, Theodore Dalamagas, H. Parkinson, F. Bancilhon, G. Flouris, Dimitris Sacharidis, P. Buneman, D. Kotzinos, Y. Stavrakas, V. Christophides, George Papastefanatos, Kostas Thiveos
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引用次数: 18

摘要

关联数据范式是一种很有前途的技术,用于发布、共享和连接Web上的数据,它为数据集成和互操作性提供了新的视角。然而,网络上分布式的、相互连接的数据源的激增对持续管理大量潜在的大型数据集及其相互依赖性提出了重大的新挑战。在本文中,我们主要关注保存不断演变的结构化互连数据的关键问题。我们认为,阻碍应用程序和用户的许多问题都与关联数据中固有的时间方面有关。我们提出了三个用例来激励我们的方法,我们讨论了出现的问题,并提出了解决方案的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diachronic linked data: towards long-term preservation of structured interrelated information
The Linked Data Paradigm is a promising technology for publishing, sharing, and connecting data on the Web, which provides new perspectives for data integration and interoperability. However, the proliferation of distributed, interconnected linked data sources on the Web poses significant new challenges for consistently managing the vast number of potentially large datasets and their interdependencies. In this article we focus on the key problem of preserving evolving structured interlinked data. We argue that a number of issues, which hinder applications and users, are related to the temporal aspect that is intrinsic in Linked Data. We present three use cases to motivate our approach, we discuss problems that occur, and propose a direction for a solution.
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