Temporal reasoning on Twitter streams using semantic web technologies

2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) Pub Date : 2015-03-23 DOI:10.1109/PERCOMW.2015.7134006

Meng Cui, W. Tai, D. O’Sullivan

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Abstract

There has been a significant increase in recent years in the volume and diversity of streams of data, data streams from sensors, data streams arising from the analysis of content or data mining, right through to user generated Twitter streams. There has been a corresponding increase in demand for more real-time analysis of these streams in order to spot significant events and trends of interest to an individual or business. This has resulted in an increased need to achieve efficient temporal reasoning upon the streams. In this paper, we present a novel approach to perform temporal reasoning on real time streams of data using Semantic Web Technologies so that we could derive more valuable information by taking account of the time dimension. Moreover, in order to deal with such high-frequency data, several filter mechanisms have been implemented to, significantly, improve the performance of the reasoning process. In order to illustrate and evaluate the approach, the real-time analysis of Twitter data is taken as a concrete use case for such data streams.

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使用语义web技术对Twitter流进行时间推理

近年来，数据流的数量和多样性都有了显著的增长，从传感器产生的数据流，从内容分析或数据挖掘产生的数据流，一直到用户生成的Twitter流。为了发现个人或企业感兴趣的重大事件和趋势，对这些流进行更多实时分析的需求也相应增加。这就增加了在流上实现有效时间推理的需求。在本文中，我们提出了一种利用语义Web技术对实时数据流进行时间推理的新方法，以便我们能够通过考虑时间维度来获得更有价值的信息。此外，为了处理这种高频数据，已经实现了几种过滤机制，以显着提高推理过程的性能。为了说明和评估该方法，将Twitter数据的实时分析作为此类数据流的具体用例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

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