The majority of data scientists and machine learning practitioners use relational data in their work [State of ML and Data Science 2017, Kaggle, Inc.]. But training machine learning models on data stored in relational databases requires significant data extraction and feature engineering efforts. These efforts are not only costly, but they also destroy potentially important relational structure in the data. We introduce a method that uses Graph Neural Networks to overcome these challenges. Our proposed method outperforms state-of-the-art automatic feature engineering methods on two out of three datasets.
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）
We describe FactorBase, a new SQL-based framework that leverages a relational database management system to support multi-relational model discovery. A multi-relational statistical model provides an integrated analysis of the heterogeneous and interdependent data resources in the database. We adopt the BayesStore design philosophy: statistical models are stored and managed as first-class citizens inside a database. Whereas previous systems like BayesStore support multi-relational inference, FactorBase supports multi-relational learning. A case study on six benchmark databases evaluates how our…
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
The validation of any database mining methodology goes through an evaluation process where benchmarks availability is essential. In this paper, we aim to randomly generate relational database benchmarks that allow to check probabilistic dependencies among the attributes. We are particularly interested in Probabilistic Relational Models (PRMs), which extend Bayesian Networks (BNs) to a relational data mining context and enable effective and robust reasoning over relational data. Even though a panoply of works have focused, separately , on the generation of random Bayesian networks and relationa…
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）
The position we advocate in this paper is that relational algebra can provide a unified language for both representing and computing with statistical-relational objects, much as linear algebra does for traditional single-table machine learning. Relational algebra is implemented in the Structured Query Language (SQL), which is the basis of relational database management systems. To support our position, we have developed the FACTORBASE system, which uses SQL as a high-level scripting language for statistical-relational learning of a graphical model structure. The design philosophy of FACTORBASE…
Aindo；University of Trieste（的里雅斯特大学）；Katholieke Universiteit Leuven（鲁汶大学）；Business Integration Partners (BIP)（商业集成伙伴公司（BIP））；Chaucer（乔叟公司）
Synthetic data generation has recently gained widespread attention as a more reliable alternative to traditional data anonymization. The involved methods are originally developed for image synthesis. Hence, their application to the typically tabular and relational datasets from healthcare, finance and other industries is non-trivial. While substantial research has been devoted to the generation of realistic tabular datasets, the study of synthetic relational databases is still in its infancy. In this paper, we combine the variational autoencoder framework with graph neural networks to generate…
Relational Deep Learning: Graph Representation Learning on Relational Databases
Stanford University（斯坦福大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Yale University（耶鲁大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
本文提出 Relational Deep Learning，将关系数据库建模为主外键连接的时序异构图，并用消息传递 GNN 端到端学习，配合 RelBench 基准实现多表数据的直接建模。
School of Computing Science（计算科学学院）；Simon Fraser University（西蒙弗雷泽大学）
Many databases store data in relational format, with different types of entities and information about links between the entities. The field of statistical-relational learning (SRL) has developed a number of new statistical models for such data. In this paper we focus on learning class-level or first-order dependencies, which model the general database statistics over attributes of linked objects and links (e.g., the percentage of A grades given in computer science classes). Class-level statistical relationships are important in themselves, and they support applications like policy making, str…
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）
University of Massachusetts（马萨诸塞大学）；Microsoft Research（微软研究院）
Instead of requiring a domain expert to specify the probabilistic dependencies of the data, in this work we present an approach that uses the relational DB schema to automatically construct a Bayesian graphical model for a database. This resulting model contains customized distributions for columns, latent variables that cluster the data, and factors that reflect and represent the foreign key links. Experiments demonstrate the accuracy of the model and the scalability of inference on synthetic and real-world data.
In this paper, we address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables. Embeddings help to capture semantics encoded in the database and can be used in a variety of settings like auto-completion of tables, fully-neural query processing of relational joins queries, seamlessly handling missing values, and more. Current work is restricted to working with just single table, or using pretrained embeddings over an external corpus making them unsuitable for use in real-world databases. In this work, we look into ways of using t…
Besides reproducing tabular data properties of standalone tables, synthetic relational databases also require modeling the relationships between related tables. In this paper, we propose the Row Conditional-Tabular Generative Adversarial Network (RC-TGAN), a novel generative adversarial network (GAN) model that extends the tabular GAN to support modeling and synthesizing relational databases. The RC-TGAN models relationship information between tables by incorporating conditional data of parent rows into the design of the child table's GAN. We further extend the RC-TGAN to model the influence t…
IBM Research（IBM研究院）；Dublin Research Laboratory（都柏林研究实验室）
Feature engineering is one of the most important but most tedious tasks in data science. This work studies automation of feature learning from relational database. We first prove theoretically that finding the optimal features from relational data for predictive tasks is NP-hard. We propose an efficient rule-based approach based on heuristics and a deep neural network to automatically learn appropriate features from relational data. We benchmark our approaches in ensembles in past Kaggle competitions. Our new approach wins late medals and beats the state-of-the-art solutions with significant m…
Most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a 'flat' form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on ind…
We address the problem of learning a distributed representation of entities in a relational database using a low-dimensional embedding. Low-dimensional embeddings aim to encapsulate a concise vector representation for an underlying dataset with minimum loss of information. Embeddings across entities in a relational database have been less explored due to the intricate data relations and representation complexity involved. Relational databases are an inter-weaved collection of relations that not only model relationships between entities but also record complex domain-specific quantitative and t…
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extracted from relational databases (RDBs). Intuitively, this joint use of SSL and GNNs should allow to leverage more of the available data, which could translate to better results. However, we found that naively porting contrastive SSL techniques can cause ``negative transfer'': linear evaluation on fixed representations from a pretrained model performs worse than on representations from the randomly-initialized model. Based on the conjecture that contrastive …
Databases contain information about which relationships do and do not hold among entities. To make this information accessible for statistical analysis requires computing sufficient statistics that combine information from different database tables. Such statistics may involve any number of {\em positive and negative} relationships. With a naive enumeration approach, computing sufficient statistics for negative relationships is feasible only for small databases. We solve this problem with a new dynamic programming algorithm that performs a virtual join, where the requisite counts are computed …
Querying databases for the right information is a time consuming and error-prone task and often requires experienced professionals for the job. Furthermore, the user needs to have some prior knowledge about the database. There have been various efforts to develop an intelligence which can help business users to query databases directly. However, there has been some successes, but very little in terms of testing and deploying those for real world users. In this paper, we propose a semantic parsing approach to address the challenge of converting complex natural language into SQL and institute a …
