Event processing under uncertainty

A. Artikis, O. Etzion, Zohar Feldman, Fabiana Fournier
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引用次数: 58

Abstract

Big data is recognized as one of the three technology trends at the leading edge a CEO cannot afford to overlook in 2012. Big data is characterized by volume, velocity, variety and veracity ("data in doubt"). As big data applications, many of the emerging event processing applications must process events that arrive from sources such as sensors and social media, which have inherent uncertainties associated with them. Consider, for example, the possibility of incomplete data streams and streams including inaccurate data. In this tutorial we classify the different types of uncertainty found in event processing applications and discuss the implications on event representation and reasoning. An area of research in which uncertainty has been studied is Artificial Intelligence. We discuss, therefore, the main Artificial Intelligence-based event processing systems that support probabilistic reasoning. The presented approaches are illustrated using an example concerning crime detection.
不确定情况下的事件处理
大数据被认为是2012年CEO们不能忽视的三大前沿技术趋势之一。大数据的特点是数量、速度、多样性和准确性(“有疑问的数据”)。作为大数据应用程序,许多新兴的事件处理应用程序必须处理来自传感器和社交媒体等源的事件,这些源具有与之相关的固有不确定性。例如,考虑不完整数据流和包含不准确数据流的可能性。在本教程中,我们将对事件处理应用程序中发现的不同类型的不确定性进行分类,并讨论事件表示和推理的含义。研究不确定性的一个领域是人工智能。因此,我们讨论了支持概率推理的主要基于人工智能的事件处理系统。所提出的方法是用一个关于犯罪侦查的例子来说明的。
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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