Sparse-Array Feature Vector Indexing for High-Dimensional Data Storage
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Solution Overview
Problem
Current data storage solutions lack native support for efficiently managing and querying high-dimensional feature vectors, leading to inefficiencies in storage and processing due to large dataset sizes and the need for extensive memory and latency in operations.
Innovation Solution
A feature vector management system that utilizes a sparse-array data structure to represent the feature space, allowing for efficient indexing and searching of high-dimensional feature vectors directly from persistent storage, reducing storage footprint and improving query response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data storage solutions are used to manage high-dimensional feature vectors, then the system can store the data, but the storage efficiency is very poor and memory overhead is significant
Solution Approach 1:
The patent segments the feature vector data into variable-length records with optimized internal structure. Each record contains only the necessary components (feature vector data, dimension count, and metadata) without redundant indexing structures, thereby improving storage efficiency while reducing memory overhead compared to traditional row-based storage formats.
Solution Approach 2:
The patent changes the storage parameters by using a custom binary format that stores feature vectors in a compact representation. The system dynamically adjusts the record structure based on the feature vector dimensions and data types, optimizing storage density and reducing the memory footprint required for storing large-scale high-dimensional data.
2Productivity
If the entire feature vector dataset is loaded from persistent storage into memory for processing, then operations can be performed on the data, but the latency is significantly increased due to persistent storage access
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing auxiliary metadata (such as dimension counts and data type information) directly in the persistent storage format. This allows the system to quickly parse and process feature vectors without requiring repeated full-data loads, thereby reducing latency while maintaining high operation speed for queries and transformations.
Solution Approach 2:
The patent extracts only the essential components needed for processing from the persistent storage format. By separating the feature vector data from redundant metadata and indexing structures, the system can load and process only the necessary data portions, reducing latency while maintaining productivity for operations like dimensionality reduction and feature extraction.
3Speed
If an index is generated for the feature vectors to enable efficient searching, then query performance improves, but significant memory overhead is required to store the index in memory
Solution Approach 1:
The patent creates a compact copy of the essential indexing information directly within the persistent storage format. Instead of maintaining separate in-memory index structures that consume significant memory, the system stores compressed metadata copies that enable fast queries while using minimal additional storage space, thereby improving query response time without increasing memory consumption.
Solution Approach 2:
The patent transforms the indexing problem by storing dimensional metadata (such as feature counts and data type information) as separate dimensional parameters in the persistent format. This dimensional organization allows the system to perform efficient queries by filtering on these dimensions without requiring large in-memory index structures, thus improving query speed while reducing memory usage.
Data Source
AI summary
In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a feature vector index, wherein the feature vector index comprises a sparse-array data structure representing a feature space for a set of labeled feature vectors, wherein the set of labeled feature vectors are assigned to a plurality of classes. The processor is to: receive a query corresponding to a target feature vector; access, via the storage device, a first portion of the feature vector index, wherein the first portion of the feature vector index comprises a subset of labeled feature vectors that correspond to a same portion of the feature space as the target feature vector; determine the corresponding class of the target feature vector based on the subset of labeled feature vectors; and provide a response to the query based on the corresponding class.