arXiv:2606.16341v1 Announce Type: new Abstract: A filtered approximate-nearest-neighbor (ANN) query returns the k nearest vectors among those satisfying an attribute predicate P of selectivity s. The best execution strategy -- pre-filter, post-filter, or in-filter -- changes with s, so a system must estimate s and choose. We model this as an argmax over a landscape with phases (regions where each strategy wins) separated by boundaries, and show that selectivity-estimation error produces plan regret -- recall lost versus the oracle strategy -- only in the critical regions around those boundarie
The increasing complexity and scale of AI models necessitate more efficient data retrieval, making granular optimization of Approximate Nearest Neighbor (ANN) search crucial for performance gains.
This research details a fundamental challenge in optimizing critical AI infrastructure, directly impacting the performance and cost-efficiency of large-scale AI applications relying on filtered ANN queries.
Understanding the 'phase transition' in filtered ANN queries allows for more robust design of database and AI systems, reducing 'plan regret' and improving search recall.
- · AI developers
- · Database architects
- · Cloud service providers
- · Researchers in machine learning
- · Systems with inefficient ANN implementations
- · Applications with high recall requirements and poor selectivity estimation
Improved performance and cost-effectiveness of AI systems using filtered ANN for data retrieval.
Faster development and deployment of more complex AI agents and applications due to better underlying data access components.
Increased adoption of AI in fields requiring precise, large-scale data querying, potentially accelerating broader AI integration.
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Read at arXiv cs.LG