Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data
Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data
复制标题
瞧：使用流时空数据进行视觉异常检测和监控
DOI：
10.1109/tvcg.2017.2744419
复制
发表时间：
2018-01-01
影响因子：
5.2
通讯作者：
Wen, Xidao
中科院分区：
文献类型：
--
作者：
Cao, Nan;Lin, Chaoguang;Wen, Xidao
不断从各种来源收集的时空数据越来越多，为及时了解数据的空间和时间背景提供了新的机会。在这样的数据中寻找异常模式是一个巨大的挑战。鉴于正常模式和异常模式之间往往没有明确的界限，现有的解决方案在以下方面的能力有限：在大型、动态和异质数据中识别异常、在其多方面的时空背景下解释异常以及允许用户在分析循环中提供反馈。在这项工作中，我们介绍了一个统一的可视化交互系统和框架，Voila，用于交互地检测从流数据源收集的时空数据中的异常。该系统旨在满足实际应用中的两个需求，即在线监控和交互。我们提出了一种新的基于张量的异常分析算法，它具有可视化和交互设计，动态地产生上下文的、可解释的数据摘要，并允许根据用户输入对异常模式进行交互排序。以“智慧城市”为例，通过定量评估和定性案例研究，验证了该框架的有效性。
The increasing availability of spatiotemporal data continuously collected from various sources provides new opportunities for a timely understanding of the data in their spatial and temporal context. Finding abnormal patterns in such data poses significant challenges. Given that there is often no clear boundary between normal and abnormal patterns, existing solutions are limited in their capacity of identifying anomalies in large, dynamic and heterogeneous data, interpreting anomalies in their multifaceted, spatiotemporal context, and allowing users to provide feedback in the analysis loop. In this work, we introduce a unified visual interactive system and framework, Voila, for interactively detecting anomalies in spatiotemporal data collected from a streaming data source. The system is designed to meet two requirements in real-world applications, i.e., online monitoring and interactivity. We propose a novel tensor-based anomaly analysis algorithm with visualization and interaction design that dynamically produces contextualized, interpretable data summaries and allows for interactively ranking anomalous patterns based on user input. Using the “smart city” as an example scenario, we demonstrate the effectiveness of the proposed framework through quantitative evaluation and qualitative case studies.