Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This article aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions are discussed. Additional resources related to event prediction are included in the paper website: http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html.

中文翻译：

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大数据时代的事件预测

事件是在特定地点、时间和语义上发生的对我们的社会或自然产生重大影响的事件，例如地震、内乱、系统故障、流行病和犯罪。非常希望能够提前预测此类事件的发生，以减少潜在的社会动荡和造成的破坏。传统上极具挑战性的事件预测现在正成为大数据时代的一种可行选择，因此正经历快速增长，这也归功于高性能计算机和新人工智能技术的进步。有大量现有工作专注于解决所涉及的挑战，包括异构多方面输出、复杂（例如，空间、时间和语义）依赖关系以及流式数据馈送。由于事件预测问题具有很强的跨学科性质，大多数现有的事件预测方法最初都是为处理特定的应用领域而设计的，尽管所使用的技术和评估程序通常可以跨不同领域推广。然而，鉴于缺乏事件预测的综合文献调查，跨不同领域交叉引用这些技术势在必行，但也很困难。本文旨在对大数据时代事件预测的技术、应用和评估进行系统全面的综述。首先，对现有技术进行了系统的分类和总结，便于领域专家寻找合适的技术，帮助模型开发人员巩固前沿研究。然后，提供主要应用领域的全面分类和总结，向模型开发人员介绍更广泛的应用，帮助他们扩大研究的影响。对评估指标和程序进行了总结和标准化，以统一利益相关者、模型开发人员和各个应用领域专家对模型性能的理解。最后，讨论了开放的问题和未来的方向。与事件预测相关的其他资源包含在论文网站：http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html。对评估指标和程序进行了总结和标准化，以统一利益相关者、模型开发人员和各个应用程序领域的领域专家对模型性能的理解。最后，讨论了开放的问题和未来的方向。与事件预测相关的其他资源包含在论文网站：http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html。对评估指标和程序进行了总结和标准化，以统一利益相关者、模型开发人员和各个应用领域专家对模型性能的理解。最后，讨论了开放的问题和未来的方向。与事件预测相关的其他资源包含在论文网站：http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html。