Acting on time-critical events by processing ever growing social media or news streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Continuous queries or techniques to search for rare events that typically arise in monitoring applications have been studied extensively for relational databases. This work is dedicated to answer the question that emerges naturally: how can we efficiently execute a continuous query on a dynamic graph? This paper presents an exact subgraph search algorithm that exploits the temporal characteristics of representative queries for online news or social media monitoring. The algorithm is based on a novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the structural and semantic characteristics of the underlying multi-relational graph. The paper concludes with extensive experimentation on several real-world datasets that demonstrates the validity of this approach.
F-IVM is a system for real-time analytics such as machine learning applications over training datasets defined by queries over fast-evolving relational databases. We will demonstrate F-IVM for three such applications: model selection, Chow-Liu trees, and ridge linear regression.
Aggregation in relational databases is accomplished through hashing and sorting interval data, which is computationally expensive and scales poorly as the data volumes grow. In this paper, we show how quantitative interval and time-series data in relational attributes can be represented using delta summary values rather than absolute values. The need for sorting to determine the row corresponding to some maximum timestamp is negated, reducing the time complexity from at least O(n log(n)) towards O(n) and improving query execution times. We illustrate this new method in the relational algebra, …
We present a new approach to e-matching based on relational join; in particular, we apply recent database query execution techniques to guarantee worst-case optimal run time. Compared to the conventional backtracking approach that always searches the e-graph "top down", our new relational e-matching approach can better exploit pattern structure by searching the e-graph according to an optimized query plan. We also establish the first data complexity result for e-matching, bounding run time as a function of the e-graph size and output size. We prototyped and evaluated our technique in the state…
With the rapid increasing of data scale, in-database analytics and learning has become one of the most studied topics in data science community, because of its significance on reducing the gap between the management and the analytics of data. By extending the capability of database on analytics and learning, data scientists can save much time on exchanging data between databases and external analytic tools. For this goal, researchers are attempting to integrate more data science algorithms into database. However, implementing the algorithms in mainstream databases is super time-consuming, espe…
Statistical-Relational Model Discovery aims to find statistically relevant patterns in relational data. For example, a relational dependency pattern may stipulate that a user's gender is associated with the gender of their friends. As with propositional (non-relational) graphical models, the major scalability bottleneck for model discovery is computing instantiation counts: the number of times a relational pattern is instantiated in a database. Previous work on propositional learning utilized pre-counting or post-counting to solve this task. This paper takes a detailed look at the memory and s…
The problem determination of Functional Dependencies between attributes Relation Scheme in the Relational Data Model. El problema de determinar Dependencias Funcionales entre atributos en los esquemas en el Modelo Relacional
How can we explore the unknown properties of high-dimensional sensitive relational data while preserving privacy? We study how to construct an explorable privacy-preserving materialized view under differential privacy. No existing state-of-the-art methods simultaneously satisfy the following essential properties in data exploration: workload independence, analytical reliability (i.e., providing error bound for each search query), applicability to high-dimensional data, and space efficiency. To solve the above issues, we propose HDPView, which creates a differentially private materialized view …
Relational In-Context Learning via Synthetic Pre-training with Structural Prior
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；University of Illinois at Urbana-Champaign（伊利诺伊大学香槟分校）；Institute of Computing Technology, Beijing University of Post（北京邮电大学计算机学院）；State Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）
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。
Analytical database systems are typically designed to use a column-first data layout to access only the desired fields. On the other hand, storing data row-first works great for accessing, inserting, or updating entire rows. Transforming rows to columns at runtime is expensive, hence, many analytical systems ingest data in row-first form and transform it in the background to columns to facilitate future analytical queries. How will this design change if we can always efficiently access only the desired set of columns? To address this question, we present a radically new approach to data transf…
