Benchmarking RL-Enhanced Spatial Indices Against Traditional, Advanced, and Learned Counterparts

2025-12-11

AI Summary

Existing Reinforcement Learning-Enhanced Spatial Indexes (RLESI) lack unified implementations and systematic evaluation under disk-based environments. This paper introduces the first modular, extensible RLESI benchmarking framework, pioneering a “training–construction decoupling” paradigm to enable fair, I/O-aware evaluation of traditional, state-of-the-art, and learned spatial indexes. The framework integrates RL training interfaces, automated hyperparameter tuning, and standardized evaluation pipelines, supporting diverse workloads—including point, range, kNN, join, and mixed read/write queries—and multidimensional metrics such as query latency, I/O cost, and index statistics. Experiments across six real-world datasets and twelve spatial indexes reveal that, despite tuning, RLESI achieves only marginal latency reduction while incurring significantly higher construction overhead and inferior overall performance compared to advanced and learned baselines. These results expose two fundamental bottlenecks: poor generalizability across workloads and prohibitively high tuning costs.

Technology Category

• Search and Optimization: Learning to Search
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Reinforcement learning has recently been used to enhance index structures, giving rise to reinforcement learning-enhanced spatial indices (RLESIs) that aim to improve query efficiency during index construction. However, their practical benefits remain unclear due to the lack of unified implementations and comprehensive evaluations, especially in disk-based settings. We present the first modular and extensible benchmark for RLESIs. Built on top of an existing spatial index library, our framework decouples index training from building, supports parameter tuning, and enables consistent comparison with traditional, advanced, and learned spatial indices. We evaluate 12 representative spatial indices across six datasets and diverse workloads, including point, range, kNN, spatial join, and mixed read/write queries. Using latency, I/O, and index statistics as metrics, we find that while RLESIs can reduce query latency with tuning, they consistently underperform learned spatial indices and advanced variants in both query efficiency and index build cost. These findings highlight that although RLESIs offer promising architectural compatibility, their high tuning costs and limited generalization hinder practical adoption.

Problem

• Benchmarking RL-enhanced spatial indices against traditional and learned counterparts
• Evaluating query efficiency and build costs across diverse workloads and datasets
• Identifying practical limitations and tuning challenges of RL-enhanced spatial indices

Innovation

• Modular benchmark for reinforcement learning-enhanced spatial indices
• Decouples index training from building with parameter tuning
• Compares RLESIs against traditional, advanced, and learned indices