An Experience Report of Large Scale Federations

Classés selon la proximité de leur titre et de leur résumé avec ceux de cet article. Sélectionnez-en pour les lire ensemble.

Les articles les plus proches

1. Apparenté Pas pertinent

   PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models

   Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik et al. · 2026 · Bases de données · Résumé IA disponible

   Résumé scientifique

   Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the diverse relational databases needed to train such models are rarely public due to privacy constraints. While there are methods to generate synthetic tabular data of arbitrary size, incorporating schema structure and primary--foreign key connectivity for multi-table generation remains challenging. Here we introduce PluRel, a framework to synthesize multi-tabular relational databases from scratch. In a step-by-step fashion, PluRel models (1) schemas with directed graphs, (2) inter-table primary-foreign key connectivity with bipartite graphs, and, (3) feature d…

2. Apparenté Pas pertinent

   Benchmarking the Fidelity and Utility of Synthetic Relational Data

   Valter Hudovernik, Martin Jurkovič, Erik Štrumbelj · 2024 · Bases de données · Résumé IA disponible

   Résumé scientifique

   Synthesizing relational data has started to receive more attention from researchers, practitioners, and industry. The task is more difficult than synthesizing a single table due to the added complexity of relationships between tables. For the same reason, benchmarking methods for synthesizing relational data introduces new challenges. Our work is motivated by a lack of an empirical evaluation of state-of-the-art methods and by gaps in the understanding of how such an evaluation should be done. We review related work on relational data synthesis, common benchmarking datasets, and approaches to measuring the fidelity and utility of synthetic data. We combine the best practices and a novel robu…

3. Apparenté Pas pertinent

   The Relational Data Borg is Learning

   Dan Olteanu · 2020 · Bases de données · Résumé IA disponible

   Résumé scientifique

   This paper overviews an approach that addresses machine learning over relational data as a database problem. This is justified by two observations. First, the input to the learning task is commonly the result of a feature extraction query over the relational data. Second, the learning task requires the computation of group-by aggregates. This approach has been already investigated for a number of supervised and unsupervised learning tasks, including: ridge linear regression, factorisation machines, support vector machines, decision trees, principal component analysis, and k-means; and also for linear algebra over data matrices. The main message of this work is that the runtime performance of…

4. Apparenté Pas pertinent

   Jekyll RDF: Template-Based Linked Data Publication with Minimized Effort and Maximum Scalability

   Natanael Arndt, Sebastian Zänker, Gezim Sejdiu et al. · 2021 · Web Engineering. ICWE 2019. Lecture Notes in Computer Science, vol 11496. Springer, Cham · Pas encore résumé

   Résumé scientifique

   Over the last decades the Web has evolved from a human-human communication network to a network of complex human-machine interactions. An increasing amount of data is available as Linked Data which allows machines to "understand" the data, but RDF is not meant to be understood by humans. With Jekyll RDF we present a method to close the gap between structured data and human accessible exploration interfaces by publishing RDF datasets as customizable static HTML sites. It consists of an RDF resource mapping system to serve the resources under their respective IRI, a template mapping based on schema classes, and a markup language to define templates to render customized resource pages. Using th…

5. Apparenté Pas pertinent

   Data Management in Microservices: State of the Practice, Challenges, and Research Directions

   Rodrigo Laigner, Yongluan Zhou, Marcos Antonio Vaz Salles et al. · 2021 · Bases de données · Résumé IA disponible

   Résumé scientifique

   We are recently witnessing an increased adoption of microservice architectures by the industry for achieving scalability by functional decomposition, fault-tolerance by deployment of small and independent services, and polyglot persistence by the adoption of different database technologies specific to the needs of each service. Despite the accelerating industrial adoption and the extensive research on microservices, there is a lack of thorough investigation on the state of the practice and the major challenges faced by practitioners with regard to data management. To bridge this gap, this paper presents a detailed investigation of data management in microservices. Our exploratory study is ba…

6. Apparenté Pas pertinent

   DataLab: A Unifed Platform for LLM-Powered Business Intelligence

   Luoxuan Weng, Yinghao Tang, Yingchaojie Feng et al. · 2024 · Bases de données · Résumé IA disponible

   Résumé scientifique

   Business intelligence (BI) transforms large volumes of data within modern organizations into actionable insights for informed decision-making. Recently, large language model (LLM)-based agents have streamlined the BI workflow by automatically performing task planning, reasoning, and actions in executable environments based on natural language (NL) queries. However, existing approaches primarily focus on individual BI tasks such as NL2SQL and NL2VIS. The fragmentation of tasks across different data roles and tools lead to inefficiencies and potential errors due to the iterative and collaborative nature of BI. In this paper, we introduce DataLab, a unified BI platform that integrates a one-sto…

7. Apparenté Pas pertinent

   Automatic Metadata Extraction for Text-to-SQL

   Vladislav Shkapenyuk, Divesh Srivastava, Theodore Johnson et al. · 2025 · Bases de données · Résumé IA disponible

   Résumé scientifique

   Large Language Models (LLMs) have recently become sophisticated enough to automate many tasks ranging from pattern finding to writing assistance to code generation. In this paper, we examine text-to-SQL generation. We have observed from decades of experience that the most difficult part of query development lies in understanding the database contents. These experiences inform the direction of our research. Text-to-SQL benchmarks such as SPIDER and Bird contain extensive metadata that is generally not available in practice. Human-generated metadata requires the use of expensive Subject Matter Experts (SMEs), who are often not fully aware of many aspects of their databases. In this paper, we e…

8. Apparenté Pas pertinent

   Metadata Caching in Presto: Towards Fast Data Processing

   Beinan Wang, Chunxu Tang, Rongrong Zhong et al. · 2022 · Bases de données · Résumé IA disponible

   Résumé scientifique

   Presto is an open-source distributed SQL query engine for OLAP, aiming for "SQL on everything". Since open-sourced in 2013, Presto has been consistently gaining popularity in large-scale data analytics and attracting adoption from a wide range of enterprises. From the development and operation of Presto, we witnessed a significant amount of CPU consumption on parsing column-oriented data files in Presto worker nodes. This blocks some companies, including Meta, from increasing analytical data volumes. In this paper, we present a metadata caching layer, built on top of the Alluxio SDK cache and incorporated in each Presto worker node, to cache the intermediate results in file parsing. The meta…