ASSED --一种通过自适应社会传感器数据过滤识别物理事件的框架
ASSED -- A Framework for Identifying Physical Events through Adaptive Social Sensor Data Filtering
摘要：
物理事件检测长期以来一直是对数字传感器数据进行操作的静态事件处理器的领域。这适用于大规模的强信号事件，如飓风，以及重要类别的事件，如地震。然而，对于各种域，存在不足的传感器覆盖，例如，山体滑坡野火和洪水社交网络提供了来自数十亿用户的海量数据，但来自这些通用社交传感器的数据包含比物理传感器多得多的噪声。社交传感器提出的最困难的挑战之一是\textit{concept drift}，其中与现象相关联的术语随着时间的推移而演变和变化，使得静态机器学习（ML）分类器效率较低。为了解决这个问题，我们开发的ASSED（自适应社会传感器事件检测）框架与基于ML的事件处理引擎，并显示它如何可以执行简单和复杂的物理事件检测强\textit{and}弱信号低延迟，高扩展性，准确的覆盖范围。具体而言，ASSED是一个框架，用于支持使用来自高置信度源（物理和注释传感器）和社交网络的流数据进行机器学习的连续过滤器生成和更新。我们建立ASSED，以支持将高置信度源集成到社交传感器事件检测中的程序，以生成高质量的过滤器，并通过跟踪其自身的性能来执行动态过滤器选择。我们展示了ASSED的能力，通过一个滑坡检测应用程序，检测到近350%的滑坡相比，静态的方法。更重要的是，ASSED自动化处理概念漂移：在初始数据汇集和分类器训练四年后，ASSED实现了0.988的事件检测准确度（无需专家手动干预），而静态方法为0.762。
英文摘要：
Physical event detection has long been the domain of static event processors operating on numeric sensor data. This works well for large scale strong-signal events such as hurricanes, and important classes of events such as earthquakes. However, for a variety of domains there is insufficient sensor coverage, e.g., landslides, wildfires, and flooding. Social networks have provided massive volume of data from billions of users, but data from these generic social sensors contain much more noise than physical sensors. One of the most difficult challenges presented by social sensors is \textit{concept drift}, where the terms associated with a phenomenon evolve and change over time, rendering static machine learning (ML) classifiers less effective. To address this problem, we develop the ASSED (Adaptive Social Sensor Event Detection) framework with an ML-based event processing engine and show how it can perform simple and complex physical event detection on strong- \textit{and} weak-signal with low-latency, high scalability, and accurate coverage. Specifically, ASSED is a framework to support continuous filter generation and updates with machine learning using streaming data from high-confidence sources (physical and annotated sensors) and social networks. We build ASSED to support procedures for integrating high-confidence sources into social sensor event detection to generate high-quality filters and to perform dynamic filter selection by tracking its own performance. We demonstrate ASSED capabilities through a landslide detection application that detects almost 350\% more landslides compared to static approaches. More importantly, ASSED automates the handling of concept drift: four years after initial data collection and classifier training, ASSED achieves event detection accuracy of 0.988 (without expert manual intervention), compared to 0.762 for static approaches.
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