Delta-Encoded Vector Embeddings for Time-Series Search
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Solution Overview
Problem
Existing systems face challenges in efficiently compressing and decompressing vector embeddings for time-series data sets, leading to high storage requirements and inefficient search operations.
Innovation Solution
A system and method that utilizes delta encoding to compress vector embeddings by storing differences between temporally-adjacent data files, allowing for efficient reconstruction and identification of changes in time-series data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If delta encoding is used to compress vector embeddings, then storage requirements are reduced, but decompression and change identification complexity increases
Solution Approach 1:
The patent segments the vector embedding data into discrete delta encoding units, where each encoding represents a incremental change from the previous vector state. This segmentation allows efficient storage by only recording differences rather than complete vectors, while the modular structure enables systematic decompression through sequential application of delta encodings.
Solution Approach 2:
The patent performs preliminary compression of vector embeddings into delta encodings during data ingestion, transforming the raw vector data into a compact differential representation before storage. This preliminary action reduces the storage burden upfront, and the system maintains the ability to reconstruct full vectors when needed by applying the stored delta encodings in sequence.
2Quantity of substance
If delta encoding is used to compress vector embeddings, then storage efficiency improves, but search operation speed may be affected
Solution Approach 1:
The patent creates reconstructed copies of vector embeddings from delta encodings when search operations are required. Instead of performing searches directly on compressed delta data, the system reconstructs the necessary vector representations on-demand, enabling efficient similarity searches while maintaining storage efficiency through the original delta-encoded format.
Solution Approach 2:
The patent implements a dynamic approach where the data representation adapts based on operational needs: delta encodings are used for storage and incremental updates, while reconstructed vectors are generated temporarily for search operations. This dynamic switching optimizes both storage efficiency and search performance by using the appropriate representation format for each operation type.
Data Source
AI summary
A method of database operations includes receiving a user query, generating a query vector embedding representative of the user query, querying a vector database using the query vector embedding, retrieving a first database vector of the plurality of database vectors based on the query and representative of a first data file corresponding to a first time and belonging to a first time-series data set, receiving a first plurality of delta encodings describing differences between vector representations of temporally-adjacent data files of the first time-series data set, identifying a second data file of the first time-series data set having a second vector representation that differs from the first database vector and corresponds to a second time, and retrieving the second data file from a database.