Parallel Hybrid Spill Tree for Scalable Nearest-Neighbor Matching
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Solution Overview
Problem
Existing techniques for finding nearest neighbors in large-scale image collections are not scalable and designed for single machines, making them inefficient for large-scale collections.
Innovation Solution
A method and system for building a parallel hybrid spill tree that partitions objects across multiple machines, using a top tree to associate objects with partitions and building leaf sub-trees as hybrid spill trees, allowing for efficient parallel nearest-neighbor matching operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing hybrid spill tree techniques are used, then nearest-neighbor search accuracy is improved, but scalability to large-scale collections deteriorates
Solution Approach 1:
The patent divides the large-scale object collection into multiple partitions, each stored on a separate server. The top tree structure segments the search space into manageable regions, allowing parallel processing across multiple machines while maintaining search accuracy within each partition.
Solution Approach 2:
The patent introduces a hierarchical dimension to the traditional hybrid spill tree by adding a top tree layer above the leaf sub-trees. This creates a two-level structure where the top tree operates in one dimension (partition selection) and leaf sub-trees operate in another dimension (detailed search), enabling scalability without sacrificing accuracy.
2Productivity
If data is distributed across multiple machines, then scalability is improved, but system complexity deteriorates
Solution Approach 1:
The top tree structure serves multiple functions: it partitions data across servers, routes queries to appropriate leaf sub-trees, and maintains a unified interface for nearest-neighbor searches. This multi-functionality reduces the need for separate management systems for each partition, simplifying the overall distributed system.
Solution Approach 2:
The top tree acts as an intermediary between the query interface and the distributed leaf sub-trees. It translates high-level search requests into partition-specific queries, managing the complexity of distributed data access while presenting a simple unified interface to users.
3Quantity of substance
If partition overlap is reduced, then storage efficiency is improved, but query accuracy deteriorates
Solution Approach 1:
The patent applies different overlap strategies to different parts of the tree structure. The top tree uses minimal or no overlap for efficient partitioning and storage, while the leaf sub-trees maintain appropriate overlap buffers to ensure query accuracy. This local differentiation optimizes both storage efficiency and search precision in their respective contexts.
Data Source
AI summary
One embodiment of the present invention provides a method and a system for building a parallel hybrid spill tree to facilitate parallel nearest-neighbor matching operations. During operation, the system receives a set of objects to be stored in the parallel hybrid spill tree. The system selects a subset of objects from the set of objects, and then uses this subset to create a “top tree.” Each node in the top tree defines an associated partition for the parallel hybrid, spill tree. The system uses the top tree to associate each object in the set of objects with a corresponding partition of the parallel hybrid spill tree. Then, the system builds for each partition of the parallel hybrid spill tree an associated “leaf sub-tree” containing the objects in the partition, with each leaf sub-tree structured as a spill tree.