Related papers: Supporting Finite Element Analysis with a Relation…
As information becomes increasingly sizable for organizations to maintain the challenge of organizing data still remains. More importantly, the on-going process of analysing incoming data occurs on a continual basis and organizations should…
Although researchers have devoted considerable attention to helping database users formulate queries, many users still find it challenging to specify queries that involve joining tables. To help users construct join queries for exploring…
PHENIX is one of the two large experiments at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory (BNL) and archives roughly 100TB of experimental data per year. In addition, large volumes of simulated data are…
This tutorial overviews principles behind recent works on training and maintaining machine learning models over relational data, with an emphasis on the exploitation of the relational data structure to improve the runtime performance of the…
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…
Interactive data-intensive applications are becoming ever more pervasive in domains such as finance, web applications, mobile computing, and Internet of Things. Increasingly, these applications are being deployed in sophisticated parallel…
Many real world systems need to operate on heterogeneous information networks that consist of numerous interacting components of different types. Examples include systems that perform data analysis on biological information networks; social…
The Forelem framework was first introduced as a means to optimize database queries using optimization techniques developed for compilers. Since its introduction, Forelem has proven to be more versatile and to be applicable beyond database…
Graph machine learning has led to a significant increase in the capabilities of models that learn on arbitrary graph-structured data and has been applied to molecules, social networks, recommendation systems, and transportation, among other…
Data discovery in data lakes with ever increasing datasets has long been recognized as a big challenge in the realm of data management, especially for semantic search of and hierarchical global catalog generation of tables. While large…
Large organizations today are being served by different types of data processing and informations systems, ranging from the operational (OLTP) systems, data warehouse systems, to data mining and business intelligence applications. It is…
A data model specifies how real-world entities and their relationships are represented and operated. In the NoSQL world data modeling usually begins from identifying application queries and designing the data model to efficiently answer…
This paper, the first step to connect relational databases with systems consequence (Kent: "System Consequence" 2009), is concerned with the semantics of relational databases. It aims to to study system consequence in the logical/semantic…
Data warehouses are nowadays an important component in every competitive system, it's one of the main components on which business intelligence is based. We can even say that many companies are climbing to the next level and use a set of…
The digital transformation of companies has led to the evolution of databases towards Big Data. Our work is part of this context and concerns more particularly the mechanisms to extract datasets stored in a Data Lake and to store the data…
Developers across the world use autonumber or auto sequences field of the backend databases for developing both the desktop and web based data centric applications which is easier to use at the development and deployment purpose but can…
Large enterprise databases can be complex and messy, obscuring the data semantics needed for analytical tasks. We propose a semantic layer in-between the database and the user as a set of small and easy-to-interpret database views,…
Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like…
This article analyzes the use of Large Language Models (LLMs) as support for the conceptual modeling of relational databases through the automatic generation of Entity-Relationship (ER) diagrams from natural language requirements. The…
Large, data centric applications are characterized by its different attributes. In modern day, a huge majority of the large data centric applications are based on relational model. The databases are collection of tables and every table…