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

ATD：协作研究：用于高频、实时异常检测的算法和数据

• 批准号: 1737987
• 负责人: Rumi Chunara
• 金额: $ 10万
• 依托单位: New York University
• 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=1737987&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数字数据量迅速增长，可以为健康和社会经济等领域的人类行为提供低成本和高分辨率的视图。来自互联网和移动连接源的个人生成的数据提供了独特的洞察力，捕获了人类行为的各个方面，而这些方面通过其他数据源是繁重的或无法量化的。此外，数据通常是实时可用的，并且可以链接到特定位置。该研究项目解决了使用此类非结构化时空数据集检测异常事件所固有的统计挑战。此类数据需要新的统计方法来预处理并从数据中提取可可靠用于事件检测的形式。此外，数据的连续性意味着异常行为的构成取决于时间尺度和潜在事件的类型。该项目旨在开发 1) 从社交媒体数据生成相关特征的方法，这些特征解释了数据的观察性质，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。该项目中的方法既适合又利用社交媒体数据的连续性和观察性。该研究将在经验数据集上进行验证，展示实用性。预计结果将适用于进一步使用公开的地理空间数据源，并了解无法通过其他方式测量的人类动态。

项目成果

Deep Landscape Features for Improving Vector-borne Disease Prediction

• 发表时间: 2019-04
• 期刊: ArXiv
• 作者: N. Rehman;U. Saif;R. Chunara

From the User to the Medium: Neural Profiling Across Web Communities

• DOI: 10.1609/icwsm.v12i1.15063
• 发表时间: 2018-06
• 期刊: ArXiv
• 作者: Mohammad Akbari;Kunal Relia;Anas Elghafari;R. Chunara

Tracking health seeking behavior during an Ebola outbreak via mobile phones and SMS

• DOI: 10.1038/s41746-018-0055-z
• 发表时间: 2018-10-02
• 期刊: NPJ DIGITAL MEDICINE
• 影响因子: 15.2
• 作者: Feng, Shuo;Grepin, Karen A.;Chunara, Rumi
• 通讯作者: Chunara, Rumi

New data paradigms: From the crowd and back

新的数据范式：从人群中来来去去

• DOI: 10.1109/bigdata.2017.8258409
• 发表时间: 2017
• 期刊: 2017 IEEE International Conference on Big Data
• 作者: Chunara, Rumi