III: Small: Temporal Relational Triples, or TR2: A Novel Data and Knowledge System for Temporal and Streaming Data

本项目利用最先进的关系机器学习技术对多数据流系统进行扩展，实现了一个综合查询、解释、推理、事件监控和行动的整体数据和知识系统。在实际应用场景中，时间和流数据通常来自多个来源，包括移动设备、物联网设备和传感器、系统日志、互联网数据、社交网络的大容量提要等。不同的数据源可能具有不同格式、结构和模式的数据，在集成这些不同的数据流源时面临的一个重大挑战是互操作性。该项目将设计一种称为时间关系三元组（TR2）的方法，它扩展了三元组存储，三元组存储在数据集成和与编码存储和知识组件的互操作性方面具有独特的优势。TR2是一项开创性的尝试，旨在弥合基于神经嵌入的现代人工智能之间的差距，现代人工智能已被证明是有效和全面的，可解释的时间和流数据分析。其结果将影响使用时间和流数据的领域，如医疗保健、银行和金融、网络安全、社交网络分析、移动和普适计算，以及用于各种目的的传感器监控。TR2旨在通过利用现代关系机器学习和表示学习来推进数据流和时态数据管理系统的前沿。它也是先前在三重存储，功能依赖，活动数据库，复杂事件处理，预测查询和规则/知识系统中的工作的概括和扩展，在一个新的整体数据系统中，所有这些都是通过编码和关系机器学习的线索紧密编织的。TR2包含以下四个关键的新理念：(1)将来自多个源的流灵活集成为时间关系三元组，(2)使用关系机器学习作为TR2的主要组成部分引入编码，并使用预测的对应项扩展数据以进行全面查询，(3)通过编码学习更高级的规则和知识，这对解释和概率推理很有用。(4)提取更高层次的状态和模式，用于持续监测关键事件的迫切性和选择行动。该项目将神经网络嵌入到数据的实体-关系视图和数据管理中心的信息模型的通用处理中。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

This project extends a multiple-data-stream system with the state-of-the-art relational machine learning, and achieves a holistic data and knowledge system for comprehensive querying, interpretation, inference, event monitoring, and actions. In a real application scenario, temporal and streaming data typically come from multiple sources including mobile, IoT devices and sensors, system logs, Internet data, high-volume feeds from social networks, among others. Different data sources may have data in different formats, structures, and schema, and a significant challenge in integrating such diverse sources of data streams is the interoperability. This project will devise an approach called temporal relational triples (TR2), which extends triple stores that have unique strengths in data integration and interoperability with coding storage and knowledge components. TR2 is a pioneering attempt to bridge the gap between neural embedding based modern AI that has proven to be effective and comprehensive and interpretable analytics of temporal and streaming data. The results will impact domains that make use of temporal and streaming data, such as healthcare, banking and finance, cybersecurity, social network analysis, mobile and pervasive computing, and sensor monitoring for various purposes.TR2 aims to advance the frontier of data stream and temporal data management systems by leveraging modern relational machine learning and representation learning. It is also a generalization and extension of previous work in triple stores, functional dependency, active databases, complex event processing, predictive queries, and rule/knowledge systems, in a novel holistic data system, all tightly woven through the threads of coding and relational machine learning. TR2 incorporates the following four key new ideas: (1) flexible integration of streams from multiple sources as temporal relational triples, (2) introducing coding using relational machine learning as a major component of TR2, and extending data with predicted counterparts for comprehensive queries, (3) learning higher-level rules and knowledge with coding, which are useful for interpretation and probabilistic inference, and (4) extracting higher-level states and patterns for continuously monitoring the imminence of critical events and for choosing actions. The project ingrains neural network embedding into a universal treatment of entity-relationship view of data and information model central to data management.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.