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

现代数据分析中最大的挑战之一是在当前大数据时代普遍存在的多模态，异构和高维多源数据集中识别微妙，复杂的异常模式（数据集的子集）。在科学、工程和商业的许多领域的重要应用中，这种显著模式的检测是知识挖掘和发现的不可或缺的工具，包括传染病爆发、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分析以及欺诈性医疗索赔等的早期检测。该项目的研究目标是开发一种新的创新范式，用于在当前大数据时代的无处不在的多模态，异构和高维多源数据集中发现复杂和微妙的异常模式。其关键思想是从统计界推广荟萃分析的思想，并将问题重新定义为对单个记录级特征进行非参数统计检验的所有子集进行搜索，以找到联合显着的子集（异常模式）。该项目的重点是与生物监测和网络安全有关的现实问题，具有两个具有挑战性的应用：罕见和传染病爆发的早期检测（例如，食源性、汉坦病毒、黄热病）和西比尔攻击（例如，垃圾邮件发送者、假冒用户和受损的正常用户）。综合教育计划包括开发计算机科学和信息学理学硕士课程提供的新课程，并向代表性不足的群体推广。本项目的研究目标是：（1）开发用于多源数据集异常信息建模的非参数检验;（2）学习非参数检验之间的异质依赖性;（3）从极大的非参数检验集合中检测异常模式;（4）从非参数检验集合中检测异常模式;（5）从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在多模态、异构和高维数据的上下文中可解释。研究方法包括：（1）对单个记录级特征进行非参数测试，这些特征提供来自多种异构输入模式（如图像、文本、视频和多个传感器流）的异常信息的一致表示;（2）能够使用未标记训练数据学习非参数测试的鲁棒分层依赖结构的深度结构化和对抗性方法;（3）能够从十亿大小的非参数测试中准确地检测显著异常模式的快速、可扩展的组合优化方法;以及（4）透明和可解释的方法，能够解释预测的异常模式，通过识别培训实例和功能，是最负责的预测。这个奖项反映了NSF的法定使命，并已被认为是值得支持，通过使用基金会的知识价值和更广泛的影响审查标准进行评估。

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.