Artificial Intelligence approaches for Energy Efficiency in RDF Storage – A Literature Review

Abstract

The rapid increase of Resource Description Framework (RDF) data creates significant energy challenges for storage and querying systems, especially under complex workloads. This survey investigates into how to improve the energy efficiency of RDF data management using Artificial Intelligence (AI), specifically methods like representation learning and reinforcement learning. While such approaches have shown significant performance improvements, mostly by reducing costly operations like joins and scans, current research frequently analyzes them only on speed and throughput with little regard for real energy consumption. The validation of their environmental impact is limited by this gap. In addition to highlighting AI’s potential to help RDF systems become more sustainable, this study urges further research to include benchmarking frameworks and energy-aware measures.