TaCo: Data-adaptive and Query-aware Subspace Collision for High-dimensional Approximate Nearest Neighbor Search

TL;DR

TaCo is a novel data-adaptive and query-aware subspace collision framework for high-dimensional approximate nearest neighbor search, significantly improving indexing speed, memory efficiency, and query throughput over existing methods.

Contribution

We introduce a data-adaptive and query-aware subspace collision method that balances index construction and query performance for high-dimensional ANNS.

Findings

Up to 8x faster indexing compared to state-of-the-art methods.

Reduces memory footprint to 0.6x of previous approaches.

Achieves over 1.5x higher query throughput.

Abstract

Approximate Nearest Neighbor Search (ANNS) in high-dimensional Euclidean spaces is a fundamental problem with broad applications. Subspace Collision is a newly proposed ANNS framework that provides a novel paradigm for similarity search and achieves superior indexing and query performance. However, the subspace collision framework remains data-agnostic and query-oblivious, resulting in imbalanced index construction and wasted query overhead. In this paper, we address these limitations from two aspects: first, we design a subspace-oriented data transformation mechanism by averaging the entropies computed over each subspace of the transformed data, which ensures balanced subspace partitioning (in an information theoretical sense) and enables data-adaptive subspace collision; second, we present query-aware and scalable query strategies that dynamically allocate overhead for each query and accelerate collision probing within subspaces. Building on these ideas, we propose a novel data-adaptive and query-aware subspace collision method, abbreviated as TaCo, which achieves efficient and accurate ANN search while maintaining an excellent balance between indexing and query performance. Extensive experiments on real-world datasets demonstrate that, when compared to state-of-the-art subspace collision methods, TaCo achieves up to 8x speedup in indexing and reduces to 0.6x memory footprint, while achieving over 1.5x query throughput. Moreover, TaCo achieves state-of-the-art indexing performance and provides an effective balance between indexing and query efficiency, even when compared with advanced methods beyond the subspace-collision paradigm. This paper was published in SIGMOD 2026.