Federated Search Models for Distributed Scientific Repositories

Abstract

A rapid growth in distributed scientific repositories has become a problem in the interconnection of heterogeneous data sources with different metadata and access requirements. The paper will outline federated search architectures that are centralized, peer-to-peer, or Hybrid, and present an Adaptive Federated Query Optimization (AFQO) model that is preoccupied with optimization of repository selection, query execution, and relevance of results. Some of the technical issues, including schema mapping, semantic interoperability, and duplicate suppression, are measured and evaluated using performance metrics such as Query Response Time (QRT), F1 score, and Duplicate Suppression Rate (DSR). Federated search systems are practically used, and case studies in environmental science, biomedical research, or astronomy prove this. The paper also notes the artificial intelligence and natural language processing that involves metadata improvement, the extension of semantics, and query optimization. The findings indicate that the adaptive optimization hybrid models can be used to increase scalability, accuracy, and user satisfaction. By creating federated search models and intelligent and interoperable systems, the work will add to the development of open science, reproducibility, and interdisciplinary collaboration.