AQEyes：用于异常检测和空气质量数据检查的可视化分析
AQEyes: Visual Analytics for Anomaly Detection and Examination of Air Quality Data
摘要：
异常检测在空气质量分析中发挥着关键作用，可以增强态势感知并提醒用户注意潜在的危险。然而，用于空气质量分析的现有异常检测方法在参数选择方面具有其自身的限制（例如，需要广泛的领域知识），计算费用，通用性（例如，需要标记的数据）、可解释性和分析效率。此外，所收集的空气质量数据质量差（格式不一致，有时缺失）也大大增加了分析的难度。在本文中，我们系统地制定了一个系统，可以解决这些限制的设计要求，然后提出AQEyes，一个集成的可视化分析系统，有效地监测，检测和检查空气质量数据中的异常。特别是，我们提出了一个统一的端到端的可调机器学习管道，其中包括几个数据预处理器和特征处理器来处理数据质量问题。该管道集成了一种高效的无监督异常检测方法，该方法在不使用标记数据的情况下工作，并克服了现有方法的局限性。此外，我们开发了一个交互式可视化系统，可视化从管道的输出。该系统采用了一套新颖的可视化和交互设计，使分析人员能够从多个层面和多个尺度直观地检查空气质量动态和异常事件。我们通过定量评估展示了该管道的性能，并使用真实世界数据集上的定性案例研究显示了可视化系统的有效性。
英文摘要：
Anomaly detection plays a key role in air quality analysis by enhancing situational awareness and alerting users to potential hazards. However, existing anomaly detection approaches for air quality analysis have their own limitations regarding parameter selection (e.g., need for extensive domain knowledge), computational expense, general applicability (e.g., require labeled data), interpretability, and the efficiency of analysis. Furthermore, the poor quality of collected air quality data (inconsistently formatted and sometimes missing) also increases the difficulty of analysis substantially. In this paper, we systematically formulate design requirements for a system that can solve these limitations and then propose AQEyes, an integrated visual analytics system for efficiently monitoring, detecting, and examining anomalies in air quality data. In particular, we propose a unified end-to-end tunable machine learning pipeline that includes several data pre-processors and featurizers to deal with data quality issues. The pipeline integrates an efficient unsupervised anomaly detection method that works without the use of labeled data and overcomes the limitations of existing approaches. Further, we develop an interactive visualization system to visualize the outputs from the pipeline. The system incorporates a set of novel visualization and interaction designs, allowing analysts to visually examine air quality dynamics and anomalous events in multiple scales and from multiple facets. We demonstrate the performance of this pipeline through a quantitative evaluation and show the effectiveness of the visualization system using qualitative case studies on real-world datasets.
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。
