RECAST: A Region-Scoped Adaptive Index for Exact Similarity Search

2026-08-07

AI Summary

This work addresses the inefficiency of existing similarity search indexes in metric spaces, which struggle to adapt to the local clustering characteristics of queries, leading to excessive redundant distance computations. The authors propose a region-aware adaptive indexing mechanism that introduces, for the first time, the concept of regional scope. By dynamically maintaining query regions and reusing previously computed distances, the method enables precise pruning. Furthermore, it intelligently detects shifts in query distribution by monitoring pruning effectiveness and recursively schedules searches into subregions to enhance efficiency. Experimental evaluation across five real-world datasets and four diverse workloads demonstrates that the proposed approach reduces distance computations by up to 64% and decreases query latency by as much as 46% compared to baseline methods such as the AV-tree.

Abstract

Similarity search in metric spaces is widely used in bioinformatics, data mining, and recommender systems. Exact similarity search is dominated by distance computations, while real query streams often concentrate in specific regions rather than spreading uniformly across the space. Pre-built indexes are constructed before the query stream and cannot adapt when queries concentrate in poorly served regions. Adaptive indexes such as AV-tree build the index from distances computed while answering queries, but discard many of those distances and do not organize the retained distances effectively for reuse. We propose RECAST, a region-scoped adaptive index for exact similarity search. RECAST maintains query regions, accumulates distances already computed while answering earlier queries (paid distances) within each region for exact pruning, and uses changes in their pruning effect to infer whether incoming queries remain concentrated in the current region. When queries shift, RECAST recursively dispatches query work to child regions, so paid distances are accumulated and reused only where they remain effective. On five real-world datasets under four workload patterns, RECAST achieves consistently lower cumulative cost than the adaptive baseline and most pre-built baselines, reducing per-query distance computations by up to 64% and query time by up to 46% compared with the state-of-the-art adaptive baseline AV-tree.

Problem

• similarity search
• adaptive indexing
• metric spaces
• query concentration
• distance computation

Innovation

• adaptive indexing
• exact similarity search
• region-scoped reuse
• paid distance accumulation
• metric space pruning