Document Vector Hashing for Efficient Similarity Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Efficiently querying large collections of documents is challenging due to the high computational cost of calculating similarity between documents, making it expensive to find documents with similar content.
Innovation Solution
A system that represents documents as vectors in a multidimensional space, maps them to hyperboxes using a set of hash functions corresponding to different scales, and stores these mappings in a hash table, allowing for efficient retrieval of similar documents by hashing and associating vectors with their corresponding hash values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If documents are stored with detailed vector representations for accurate similarity calculation, then query accuracy is improved, but storage space and query time increase
Solution Approach 1:
The patent segments the continuous multidimensional space into discrete hyperboxes at multiple scales. Each hyperbox represents a region containing documents with similar characteristics. By organizing documents into these segmented regions, the system enables efficient retrieval without requiring comparison with all documents, thus reducing query time while maintaining accuracy through multi-scale granularity.
Solution Approach 2:
The patent introduces a hierarchical scale dimension to organize hyperboxes. Documents are organized not only by their content vectors but also by their position in the hierarchical scale structure. This additional dimensional organization allows the system to quickly navigate to relevant document groups at appropriate scales, reducing the search space and query time while preserving measurement accuracy.
2Device complexity
If a fixed hash table structure is used for storing document vectors, then data structure simplicity is improved, but adaptability to varying document collections deteriorates
Solution Approach 1:
The patent implements a dynamic hash table structure where hyperboxes can be created, merged, or split based on the characteristics of the document collection. The hash table adapts its structure dynamically as documents are added or removed, allowing the system to maintain optimal organization without requiring complete reorganization. This dynamic structure preserves simplicity while providing high adaptability to varying collection sizes and content distributions.
Solution Approach 2:
The patent changes the parameters of the hash table structure, specifically the number and size of hyperboxes, based on the document collection characteristics. The system can adjust the granularity and distribution of hyperboxes to match the actual data distribution, enabling the simple hash table structure to adapt to diverse and changing document collections efficiently.
3Measurement precision
If multiple scales are used to define hyperboxes for better query precision, then query accuracy is improved, but hash table complexity increases
Solution Approach 1:
The patent implements a nested hierarchy ofhyperboxes where smaller-scalehyperboxes are contained within or related to larger-scalehyperboxes. This nesting structure allows the system to organize documents at multiple scales in a hierarchical manner, where each level provides different granularity. The nested structure enables efficient multi-scale queries without requiring completely separate hash tables for each scale, thus improving query precision while controlling complexity through hierarchical organization.
Data Source
AI summary
A system for storing document collections in a manner that facilitates efficient querying. Each document vector is hashed, by applying a suitable hash function to the components of the vector. The hash function maps the vector to a particular hash value, corresponding to a particular hyperbox in the multidimensional space to which the vectors belong. The vector, or a pointer to the vector, is then stored in a hash table in association with the vector's hash value. Subsequently, given a document of interest, documents similar to the document of interest may be found by hashing the vector of the document of interest, and then returning the vectors that are associated, in the hash table, with the resulting hash value.