The rapid evolution of learned data structures has revolutionized database indexing, particularly for sorted integer datasets. While learned indexes excel in static scenarios due to their low memory footprint, reduced storage requirements, and fast lookup times, benchmarks like SOSD and TLI have largely overlooked compressed indexes and SIMD-based implementations of traditional indexes. This paper addresses this gap by introducing a comprehensive benchmarking framework that (i) evaluates traditional, learned, and compressed indexes across 12 datasets (real and synthetic) of varying types and sizes; (ii) integrates state-of-the-art SIMD-enhanced B-Tree variants; and (iii) measures critical performance metrics such as memory usage, construction time, and lookup efficiency. Our findings reveal that while learned indexes minimize memory usage, a feature useful when internal memory constraints are mandatory, SIMD-enhanced B-Trees consistently achieve superior lookup times with comparable extra space. On the other hand, compressed indexes like LA-vector and EliasFano provide very effective compression of the indexed data with slower access speeds (2x-3x). Another contribution of this paper is a publicly available benchmarking framework (composed of code and datasets) that makes our experiments reproducible and extensible to other indexes and datasets.

MOTIVATION: The rapid increase of bio-medical literature makes it harder and harder for scientists to keep pace with the discoveries on which they build their studies. Therefore, computational tools have become more widespread, among which network analysis plays a crucial role in several life-science contexts. Nevertheless, building correct and complete networks about some user-defined biomedical topics on top of the available literature is still challenging. RESULTS: We introduce NetMe 2.0, a web-based platform that automatically extracts relevant biomedical entities and their relations from a set of input texts-i.e. in the form of full-text or abstract of PubMed Central's papers, free texts, or PDFs uploaded by users-and models them as a BioMedical Knowledge Graph (BKG). NetMe 2.0 also implements an innovative Retrieval Augmented Generation module (Graph-RAG) that works on top of the relationships modeled by the BKG and allows the distilling of well-formed sentences that explain their content. The experimental results show that NetMe 2.0 can infer comprehensive and reliable biological networks with significant Precision-Recall metrics when compared to state-of-the-art approaches. AVAILABILITY AND IMPLEMENTATION: https://netme.click/.