ETD

• data mining
• distributed systems
• machine learning
• software engineering

The continuously evolving world of edge computing has led to the creation of numerous platforms for managing computational resources and data in the edge context. This thesis seeks to establish a basis for new strategies that implement Data Fetching and Data Discovery approaches within the context of U.S. national platforms, specifically the National Data Platform and Sage Continuum.

Through a theoretical and subsequently practical approach, the work aims to improve these platforms to enhance the general user experience. This includes allowing almost immediate access to data based on its current utility and meeting researchers' needs for real-time data analysis and search.

The proposed techniques and architectural designs attempt to experimentally test and validate these strategies, considering both available resources and efficiency results. Therefore, this thesis acts as a foundation for applications that require significant refinement and further study in specific branches such as distributed systems, machine learning models, data mining, and software engineering.