TL;DR
The paper outlines the key features of SDMX that make it suitable for this role and presents a high-level architecture for the proposed component.
Croissant has emerged as a standard for machine-readable dataset metadata, yet populating its fields remains labor-intensive and requires careful reading of accompanying dataset documentation. We present the first benchmark enabling end-to-end evaluation of metadata extraction aligned with a community-standard schema....
Modern day software products based on data usually rely on spreadsheet datasets which might be poorly structured and dirty. The current paper outlines the creation of a pipeline automation system called ReFyn, which is aimed at the effective transformation of diverse data formats such as CSV files and Excel sheets. Dat...
Tools and methods for the automated collection, processing, and integration of data for scientific analytical systems in critical infrastructure are analyzed. A three-level methodol-ogy is proposed, which includes: collecting information from official, commercial, and unofficial APIs, web resources, and streaming platf...
This study addresses the need to update professional profiles in higher education in response to the emergence of Artificial Intelligence (AI) and data science. The objective of this research was to implement a computational data pipeline based on an Extract, Transform, and Load (ETL) approach to automatically assess t...
A metadata-driven automation framework that leverages AI-assisted refactoring and Python-based pipelines to accelerate migration while maintaining functional correctness is presented, providing a scalable blueprint for enterprise-wide BI modernization.
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