UREM: A High-performance Unified and Resilient Enhancement Method for Multi- and High-Dimensional Indexes

2025-10-22

AI Summary

Existing index enhancement methods face a “static–dynamic” performance trade-off: structure-oriented approaches achieve high static efficiency but suffer from poor generalizability and weak dynamic adaptability; layout-oriented methods offer broad applicability and dynamic robustness at the cost of degraded static performance. This paper proposes UREM—the first unified, adaptive enhancement framework for multi- and high-dimensional indexes. UREM adopts a structure-agnostic design, integrating fine-grained layout optimization with lightweight partial reorganization to seamlessly support 20 mainstream index structures across diverse platforms. Under static workloads, it achieves up to 5.73× speedup for point queries and 9.18× for range queries; under dynamic workloads, it delivers average speedups of 5.72× and 9.47×, respectively—outperforming state-of-the-art methods even for several conventional indexes. UREM is the first approach to simultaneously achieve generality, high static performance, and dynamic robustness.

Technology Category

• Search and Optimization: Distributed Search
• Reasoning under Uncertainty: Stochastic Optimization
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Numerous multi- or high-dimensional indexes with distinct advantages have been proposed on various platforms to meet application requirements. To achieve higher-performance queries, most indexes employ enhancement methods, including structure-oriented and layout-oriented enhancement methods. Existing structure-oriented methods tailored to specific indexes work well under static workloads but lack generality and degrade under dynamic workloads. The layout-oriented methods exhibit good generality and perform well under dynamic workloads, but exhibit suboptimal performance under static workloads. Therefore, it is an open challenge to develop a unified and resilient enhancement method that can improve query performance for different indexes adaptively under different scenarios. In this paper, we propose UREM, which is the first high-performance Unified and Resilient Enhancement Method designed for both multi- and high-dimensional indexes, capable of adapting to different scenarios. Specifically, UREM (1) can be uniformly applied with different indexes on various platforms; (2) enhances the query performance of indexes by layout optimization under static workloads; (3) enables indexes to stabilize performance when queries shift through partial layout reorganization. We evaluate UREM on 20 widely used indexes. Experimental results demonstrate that UREM improves the query performance of multi- and high-dimensional indexes by up to 5.73x and 9.18x under static workloads, and by an average of 5.72x and 9.47x under dynamic workloads. Moreover, some traditional indexes enhanced by UREM even achieve performance comparable to or even surpassing that of recent advanced indexes.

Problem

• Developing a unified enhancement method for multi-dimensional indexes
• Adapting to both static and dynamic workload scenarios
• Improving query performance across various index types

Innovation

• Unified enhancement method for multi-dimensional indexes
• Layout optimization for static workload performance
• Partial layout reorganization for dynamic stability