[论文解读] Event Prediction in the Big Data Era: A Systematic Survey

Liang Zhao|arXiv (Cornell University)|Jul 19, 2020

Big Data and Business Intelligence被引用 14

一句话总结

本文系统性地综述了大数据时代背景下的事件预测，整合了跨不同领域的技术、应用与评估方法。它提出了一套统一的事件预测方法分类体系，强调了多维输出与复杂依赖关系等挑战，并指出了未来关键研究方向，包括多目标优化与反事实分析。

ABSTRACT

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as civil unrest, system failures, and epidemics. It is highly desirable to be able to anticipate the occurrence of such events in advance in order 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. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex dependencies, and streaming data feeds. 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 paper 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. 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 for this promising and important domain are elucidated and discussed.

研究动机与目标

• 为跨学科领域中事件预测缺乏全面文献综述提供解决方案。
• 基于问题建模与方法论，对现有事件预测技术进行系统化与分类整理。
• 为模型开发者与领域专家提供主要应用领域的分层分类体系。
• 统一不同应用场景下的评估指标与流程，实现性能评估的一致性。
• 识别开放性问题与未来研究方向，以推动人工智能与大数据领域中事件预测的发展。

提出的方法

• 基于所预测事件要素的类型（时间、地点、语义）与所用方法论，提出系统化的事件预测技术分类体系。
• 将方法划分为混合模型、数据驱动模型与因果模型三类，强调其优势与局限性。
• 将机器学习、数据挖掘、自然语言处理、统计学与模式识别等技术整合进统一框架。
• 分析多维预测挑战，包括时间、地点、主题、强度与持续时间的联合建模。
• 提出需更深入地将因果原理融入数据驱动模型，以提升模型的鲁棒性与泛化能力。
• 倡导采用多目标优化与反事实/预测性分析，以支持可操作的决策制定。

实验结果

研究问题

• RQ1 如何在不同领域间系统性地对事件预测技术进行分类与比较？
• RQ2 预测异构、多维事件输出（如时间、地点、主题、强度）的关键挑战是什么？
• RQ3 如何在预测系统中有效建模事件之间的复杂依赖关系（如级联效应）？
• RQ4 在多样化事件预测应用中，最恰当且标准化的评估指标与流程是什么？
• RQ5 在多目标与预测性设置下，哪些未来研究方向对推动事件预测发展最为关键？

主要发现

• 分析了超过200篇近期事件预测相关文献，揭示该领域因大数据与人工智能的进步而快速发展。
• 事件预测仍是多目标问题，涉及准确性、提前时间、分辨率、置信度与效率之间的权衡。
• 当前模型多依赖集成方法融合预测结果，而非深度整合因果原理与数据驱动学习。
• 亟需通用的、跨领域的框架，以同时支持预测与预测性分析。
• 反事实分析与预测性分析尚不成熟，但对实现公共卫生与灾害管理等领域的主动决策至关重要。
• 标签质量以及在噪声或损坏标注下的鲁棒性，仍是多维事件预测中的关键挑战。