This paper discusses a method for implementing a probabilistic inference system based on an extended relational data model. This model provides a unified approach for a variety of applications such as dynamic programming, solving sparse linear equations, and constraint propagation. In this framework, the probability model is represented as a generalized relational database. Subsequent probabilistic requests can be processed as standard relational queries. Conventional database management systems can be easily adopted for implementing such an approximate reasoning system.
Method for the semantic indexing of concept hierarchies, uniform representation, use of relational database systems and generic and case-based reasoning
TU Dresden（德累斯顿工业大学）；Frauenhofer Institut für Intelligente Analyse und Informationssysteme IAIS（弗劳恩霍夫智能分析与信息系统研究所IAIS）
Probabilistic Relational Model Benchmark Generation
LARODEC Laboratory（拉罗德克实验室）；ISG（突尼斯高等管理学院）；Université de Tunis（突尼斯大学）；DUKe research group（DUKe研究组）；LINA Laboratory UMR 6241（LINA实验室（法国国家科研中心联合研究单位6241））；University of Nantes（南特大学）；DataForPeople
Supervised Learning on Relational Databases with Graph Neural Networks
Amazon Web Services（亚马逊云服务）
大多数数据科学家和机器学习从业者在工作中使用关系数据[State of ML and Data Science 2017，Kaggle，Inc.]。但是在存储在关系数据库中的数据上训练机器学习模型需要大量的数据提取和特征工程工作。这些工作不仅代价高昂，而且还破坏了数据中潜在的重要关系结构。我们介绍了一种方法，使用图神经网络来克服这些挑战。我们提出的方法在三个数据集中的两个数据集上的性能优于最先进的自动特征工程方法。
Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks
University of Stuttgart, Stuttgart, Germany（斯图加特大学）；Internet Science Research Group, University of Southampton, Southampton, United Kingdom（互联网科学研究组，南安普顿大学）
AskDB: An LLM Agent for Natural Language Interaction with Relational Databases
IT Operations, Mantu Group（Mantu集团信息技术部）；Faculty of Engineering, Vietnamese-German University（越南-德国大学工程学院）；Computer Science and Information Engineering, National Taiwan University（国立台湾大学计算机科学与工程系）
Serving Deep Learning Model in Relational Databases
Arizona State University（亚利桑那州立大学）；Amazon（亚马逊公司）；IBM T. J. Watson Research Center（IBM T. J. 沃森研究中心）；Hong Kong University of Science and Technology（香港科技大学）；Lawrence Berkeley National Lab（劳伦斯伯克利国家实验室）
Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases
Department of Data Science and Artificial Intelligence（数据科学与人工智能系）；Hong Kong Polytechnic University（香港理工大学）；School of Computer Science and Engineering（计算机科学与工程学院）；Central South University（中南大学）；School of Information and Communication Technology（信息与通信技术学院）；Griffith University（格里菲斯大学）；National Super Computing Center（国家超级计算中心）
