Order matters! Harnessing a world of orderings for reasoning over massive data
Order matters! Harnessing a world of orderings for reasoning over massive data
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DOI：
10.3233/sw-2012-0085
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发表时间：
2013-01-01
期刊：
影响因子：
3
通讯作者：
Horrocks, Ian
中科院分区：
文献类型：
--
作者：
Della Valle, Emanuele;Schlobach, Stefan;Horrocks, Ian
越来越多的应用程序需要实时处理大量的、动态生成的、有序的数据;顺序是一个重要因素，因为它反映了新近性或相关性。语义技术有可能无法满足这些应用程序的需求，因为它们没有配备适当的工具来回答对大量，高度动态，有序数据集的查询。在这篇愿景论文中，我们认为，一些数据管理技术应该出口到语义技术的背景下，通过整合排序与推理，并通过使用流和排名感知数据管理的启发方法。我们系统地探索问题空间，并指出已成功解决的问题和仍然需要基础研究的问题，试图刺激和引导语义技术的范式转变。
More and more applications require real-time processing of massive, dynamically generated, ordered data; order is an essential factor as it reflects recency or relevance. Semantic technologies risk being unable to meet the needs of such applications, as they are not equipped with the appropriate instruments for answering queries over massive, highly dynamic, ordered data sets. In this vision paper, we argue that some data management techniques should be exported to the context of semantic technologies, by integrating ordering with reasoning, and by using methods which are inspired by stream and rank-aware data management. We systematically explore the problem space, and point both to problems which have been successfully approached and to problems which still need fundamental research, in an attempt to stimulate and guide a paradigm shift in semantic technologies.