ATD: Collaborative Research: Algorithms and Data for High-Frequency, Real-Time Anomaly Detection

ATD：协作研究：高频实时异常检测的算法和数据

• 批准号: 1737673
• 负责人: Tyler McCormick
• 金额: $ 10万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737673&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; Algorithms; Data

The rapidly burgeoning amount of digital data from Internet and mobile-enabled communications can offer low-cost and high-resolution views into human behavior across areas such as health and socio-economics. Personally-generated data from Internet and mobile-connected sources offer unique insight, capturing aspects of human behavior that would be taxing or impossible to quantify through other data sources. Moreover, the data is often available in real time and can be linked to specific locations. This research project addresses the statistical challenges inherent in using such unstructured spatio-temporal data sets for detection of anomalous events.Such data requires new statistical approaches to pre-process and extract forms from the data that can reliably be used for event detection. Further, the continuous nature of the data means that what constitutes anomalous behavior depends on the time-scale and on the type of underlying event. This project aims to develop 1) approaches for generating relevant features from social media data that account for the observational nature of the data and can be used in spatio-temporal models of real-world behavior and 2) a new multi-scale approach to modeling dependence structures that uses new information to continuously refine the model and accurately assess anomalies. The approach in this project is both suited to and harnesses the continuous and observational nature of social media data. The research will be validated on empirical data sets, demonstrating practical utility. It is anticipated that the results will be applicable to further the use of publicly-available geospatial data sources and understand human dynamics that are not measurable through other means.

来自互联网和移动通信的快速增长的数字数据量可以提供低成本和高分辨率的视角，以了解健康和社会经济等领域的人类行为。来自互联网和移动连接来源的个人生成数据提供了独特的见解，捕捉了通过其他数据源难以或无法量化的人类行为方面。此外，这些数据通常是实时的，可以链接到特定的位置。本研究项目解决了使用这种非结构化时空数据集检测异常事件所固有的统计挑战。这些数据需要新的统计方法来预处理并从数据中提取可可靠地用于事件检测的表单。此外，数据的连续性意味着构成异常行为的因素取决于时间尺度和潜在事件的类型。该项目旨在开发1)从社交媒体数据中生成相关特征的方法，这些特征可以解释数据的观测性质，并可用于现实世界行为的时空模型；2)一种新的多尺度方法来建模依赖结构，该方法使用新信息不断完善模型并准确评估异常。这个项目中的方法既适合也利用了社交媒体数据的连续性和可观察性。该研究将在实证数据集上进行验证，以证明其实际效用。预计研究结果将适用于进一步利用公开的地理空间数据源，并了解无法通过其他手段测量的人类动态。

项目成果

Bayesian Joint Spike-and-Slab Graphical Lasso

• 发表时间: 2018-05
• 期刊: Proceedings of machine learning research
• 作者: Z. Li;T. McCormick;S. Clark

Beyond Prediction: A Framework for Inference With Variational Approximations in Mixture Models

超越预测：混合模型中变分近似的推理框架

• DOI: 10.1080/10618600.2019.1609977
• 发表时间: 2019
• 期刊: Journal of Computational and Graphical Statistics
• 影响因子: 2.4
• 作者: Westling, T.;McCormick, T. H.
• 通讯作者: McCormick, T. H.

Modeling recovery curves with application to prostatectomy

• DOI: 10.1093/biostatistics/kxy002
• 发表时间: 2019-10-01
• 期刊: BIOSTATISTICS
• 影响因子: 2.1
• 作者: Wang, Fulton;Rudin, Cynthia;Gore, John L.
• 通讯作者: Gore, John L.