大数据中小概率事件的重要性：信息度量、应用与挑战
Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges
AI总结：
本文从信息论角度综述大数据中小概率稀有事件的重要性度量理论与模型，梳理其在消息处理和数据分析中的应用，并指出面向智慧城市、自动驾驶和物联网异常检测的未来挑战。
摘要：
在智慧城市的许多应用（例如异常检测和安全系统）中，稀有事件主导着物联网（IoTs）所收集大数据总信息的重要性。也就是说，在海量数据中，挖掘与少数子集所包含的稀有事件相关的有价值信息至关重要。为此，如何从信息论的角度有效度量小概率事件所具有的重要信息，是一个基础性问题。本文首先综述了与重要性度量有关的若干理论和模型，并研究了主观或语义重要性与大数据中稀有事件之间的关系。此外，文章从信息度量的视角讨论了消息处理和数据分析方面的一些应用。另外，基于稀有事件检测，介绍了与信息度量相关的若干开放性挑战，例如智慧城市、自动驾驶以及 IoTs 中的异常检测，这些可被视为未来的研究方向。
英文摘要：
In many applications (e.g., anomaly detection and security systems) of smart cities, rare events dominate the importance of the total information of big data collected by Internet of Things (IoTs). That is, it is pretty crucial to explore the valuable information associated with the rare events involved in minority subsets of the voluminous amounts of data. To do so, how to effectively measure the information with importance of the small probability events from the perspective of information theory is a fundamental question. This paper first makes a survey of some theories and models with respect to importance measures and investigates the relationship between subjective or semantic importance and rare events in big data. Moreover, some applications for message processing and data analysis are discussed in the viewpoint of information measures. In addition, based on rare events detection, some open challenges related to information measures, such as smart cities, autonomous driving, and anomaly detection in IoTs, are introduced which can be considered as future research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
