III: Small: Collaborative Research: A novel paradigm for detecting complex anomalous patterns in multi-modal, heterogeneous, and high-dimensional multi-source data sets

现代数据分析的最大挑战之一是在当前大数据时代无处不在的多模式、异质和高维的多源数据集中识别微妙、复杂的异常模式(数据集的新奇或意想不到的子集)。这种显著模式的检测是在科学、工程和商业的许多领域的重要应用中进行知识挖掘和发现的不可或缺的工具，包括早期检测传染病爆发、犯罪热点、网络入侵、虚假广告、网络僵尸网络、客户活动监控和用户概况以及欺诈性医疗索赔等。该项目的研究目标是开发一种新的创新范式，在当前大数据时代无处不在的多模、异质、高维的多源数据集中发现复杂而细微的异常模式。其关键思想是从统计界推广Meta分析的概念，并将问题重新定义为对对单个记录级特征进行的非参数统计检验的所有子集的搜索，以便找到共同重要的子集(异常模式)。该项目专注于与生物监控和网络安全相关的现实世界问题，具有两个具有挑战性的应用：早期检测罕见和传染性疾病的爆发(例如食源性疾病、汉坦病毒、黄热病)和Sybil攻击(例如垃圾邮件发送者、虚假用户和受影响的正常用户)。综合教育计划包括开发在理科硕士课程中开设的计算机科学和信息学方面的新课程，以及面向代表性不足的群体。该项目的研究结果将通过教程和工作坊广泛传播给更广泛的受众。本项目的研究目标是：(1)开发用于建模多源数据集的异常信息的非参数检验；(2)学习非参数检验之间的异质依赖关系；(3)从非常大的非参数检验集合中检测异常模式；以及(4)使检测到的异常模式能够在多模式、异质和高维数据的背景下解释。该研究方法包括：(1)对单个记录级特征的非参数检验，其提供来自多个异类输入模式(例如图像、文本、视频和多传感器流)的异常信息的一致表示；(2)能够使用未标记的训练数据学习非参数检验的健壮的分层依赖结构的深层结构化和对抗性方法；(3)能够准确地从十亿大小的非参数检验中检测显著异常模式的快速、可伸缩的组合优化方法；以及(4)透明和可解释的方法，能够通过识别对预测负有最大责任的训练实例和特征来解释预测的异常模式。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

One of the greatest challenges in modern data analysis is to identify subtle, complex anomalous patterns (subsets of a data set that are novel or unexpected) within ubiquitous multi-modal, heterogeneous, and high-dimensional multi-source data sets in the current big data era. The detection of such salient patterns is an indispensable tool for knowledge mining and discovery in important applications across many fields of science, engineering, and business, including the early detection of infectious disease outbreaks, crime hotspots, network intrusions, false advertising, cyber botnets, customer activity monitoring and user profiling, and fraudulent medical claims, among others. The project research goal is to develop a new and innovative paradigm for discovering complex and subtle anomalous patterns in ubiquitous multi-modal, heterogeneous, and high-dimensional multi-source datasets in the current big data era. The key idea is to generalize the idea of meta-analysis from the statistical community and to reframe the problem as a search over all subsets of nonparametric statistical tests that are conducted on individual record-level features, in order to find the subsets (anomalous patterns) that are jointly significant. The project is focused on real-world problems related to biosurveillance and cybersecurity with two challenging applications: early detection of rare and infectious disease outbreaks (e.g., foodborne, Hantavirus, yellow fever) and Sybil attacks (e.g., spammers, fake users, and compromised normal users). The integrated education plan includes the development of new courses offered at the Master of Science program in Computer Science and Informatics and outreach to underrepresented groups. The outcomes of this project will be widely disseminated to broader audience via tutorials and workshops.The research objectives of this project are: (1) the development of nonparametric tests for modeling anomalous information of multi-source datasets; (2) learning heterogeneous dependencies among nonparametric tests; (3) detecting anomalous patterns from an extremely large set of nonparametric tests; and (4) making the detected anomalous patterns interpretable in the context of multi-modal, heterogeneous, and high-dimensional data. The research approach includes the development of (1) nonparametric tests on individual record level features that provide consistent representations of anomalous information from multiple heterogeneous input modalities, such as image, text, video, and multiple sensor streams; (2) deep structured and adversarial methods capable of learning robust hierarchical dependency structures of nonparametric tests using unlabeled training data; (3) fast, scalable combinatorial optimization methods capable of accurately detecting salient anomalous patterns from billion-size nonparametric tests; and (4) transparent and interpretable methods capable of explaining the predicted anomalous patterns by identifying training instances and features that are most responsible for the predictions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.