The field of recommender systems and data processing is moving towards a more sustainable and efficient direction. Researchers are investigating the environmental impact of ensemble techniques and exploring ways to optimize model efficiency through quantization and bit depth optimization. The focus is on developing methods that can reduce energy consumption and carbon footprint while maintaining or improving model accuracy. Notable papers in this area include: The Environmental Impact of Ensemble Techniques in Recommender Systems, which investigates the energy consumption of ensemble methods and identifies selective strategies as more efficient than exhaustive averaging. Dimension vs. Precision: A Comparative Analysis of Autoencoders and Quantization for Efficient Vector Retrieval, which provides a practical guide for deploying efficient retrieval systems by evaluating the trade-offs between dimensionality reduction and precision reduction. Other notable works include the development of novel indexing algorithms, such as B+ANN, and the systematic evaluation of plan-based adaptive query processing, which provide insights into improving query execution mechanisms and reducing performance bottlenecks.