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

III：小：时态关系三元组，或 TR2：用于时态和流数据的新颖数据和知识系统

• 批准号: 2124704
• 负责人: Tingjian Ge
• 金额: $ 47.13万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124704&HistoricalAwards=false
• 关键词: III; Small; Temporal; Relational; Triples

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.

该项目通过最先进的关系机器学习扩展了多数据流系统，并实现了用于全面查询、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，时态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、来自社交网络的大量源等。不同的数据源可能具有不同格式、结构和模式的数据，集成如此不同的数据流源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，该方法扩展了在数据集成以及与编码存储和知识组件的互操作性方面具有独特优势的三元组存储。 TR2 是弥合基于神经嵌入的现代人工智能之间差距的开创性尝试，它已被证明是对时态和流数据的有效、全面和可解释的分析。结果将影响使用时态和流数据的领域，例如医疗保健、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 旨在推进数据流和流数据的前沿发展。利用现代关系机器学习和表示学习的时态数据管理系统。它也是之前在三重存储、函数依赖、活动数据库、复杂事件处理、预测查询和规则/知识系统方面的工作的概括和扩展，在一个新颖的整体数据系统中，所有这些都通过编码和关系的线程紧密地交织在一起。机器学习。 TR2 融合了以下四个关键新想法：(1) 将多个来源的流灵活集成为时间关系三元组，(2) 引入使用关系机器学习的编码作为 TR2 的主要组成部分，并使用预测对应项扩展数据以进行全面查询， （3）通过编码学习更高级别的规则和知识，这对于解释和概率推理很有用；（4）提取更高级别的状态和模式，以持续监控关键事件的迫近性并选择行动。该项目将神经网络嵌入到对数据实体关系视图和数据管理核心信息模型的普遍处理中。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

RL2: A Call for Simultaneous Representation Learning and Rule Learning for Graph Streams

RL2：呼吁同时进行图流表示学习和规则学习

• DOI: 10.1145/3534678.3539309
• 发表时间: 2022-01
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’22
• 作者: Liu, Qu;Ge, Tingjian
• 通讯作者: Ge, Tingjian

Reduction of large-scale graphs: Effective edge shedding at a controllable ratio under resource constraints

大规模图的缩减：资源约束下可控比例的有效边缘脱落

• DOI: j.knosys.2022.108126
• 发表时间: 2022-01
• 期刊: Knowledgebased systems
• 作者: Zeng, Yiling;Song, Chunyao;Ge, Tingjian;Zhang, Ying
• 通讯作者: Zhang, Ying

Fairness-Aware Continuous Predictions of Multiple Analytics Targets in Dynamic Networks

动态网络中多个分析目标的公平感知连续预测

• DOI: 10.1145/3580305.3599341
• 发表时间: 2023-08
• 期刊: KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Liu, Ruifeng;Liu, Qu;Ge, Tingjian
• 通讯作者: Ge, Tingjian