Codeword Arithmetic on Compacted Data Without Decompression
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Solution Overview
Problem
Current data management systems face challenges in performing arithmetic and logical operations on compressed data without decompression, leading to computational bottlenecks and inefficiencies, particularly in real-time analytics and database operations.
Innovation Solution
A system and method that utilizes an enhanced reference codebook storing arithmetic metadata for each codeword, enabling direct performance of arithmetic operations on compacted data without decompression, using a codeword arithmetic engine and semantic relationship table to maintain storage efficiency and random-access capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is compressed to reduce storage space, then storage efficiency is improved, but the data cannot be accessed randomly and must be decompressed before processing
Solution Approach 1:
The patent segments the compressed data into fixed-width codewords with defined boundaries, allowing the system to locate and access specific codewords within the compressed stream without decompressing the entire dataset. Each codeword is structured with a fixed bit width that enables precise positioning through bit-level calculations.
Solution Approach 2:
The patent performs preliminary actions by pre-defining codeword boundaries and structures during the compression phase. This preliminary structuring allows subsequent random access operations to directly locate codewords using bit position calculations without requiring full decompression, effectively preparing the data for efficient access in advance.
2Ease of operation
If compressed data is decompressed before processing, then arithmetic operations can be performed, but processing time and temporary storage requirements increase
Solution Approach 1:
The patent extracts only the necessary arithmetic metadata (numeric values, data types, sort orders) from the compressed codewords and stores it in an enhanced codebook. This extraction allows arithmetic operations to be performed on the metadata directly without requiring full decompression of the sourceblocks, significantly reducing processing time and temporary storage needs.
Solution Approach 2:
The patent introduces an intermediary enhanced codebook that stores arithmetic metadata alongside codeword mappings. This intermediary structure acts as a bridge between the compressed data and arithmetic operations, enabling computations to be performed on the metadata without accessing or decompressing the full sourceblocks.
3Quantity of substance
If traditional compression is used to save storage space, then storage efficiency improves, but transmission bandwidth remains a bottleneck for large datasets
Solution Approach 1:
The patent merges data compression with arithmetic metadata preservation in a unified codebook structure. By combining the codeword mappings and arithmetic metadata (numeric values, data types, sort orders) into a single enhanced codebook, the system achieves both space efficiency and operational capability, allowing compressed data to be both compact and computationally usable.
Data Source
AI summary
A system and method for performing arithmetic operations on compacted data files. The system receives data queries containing arithmetic operations to be performed on compressed data. Using an estimation process, the system locates a starting position in the compacted file and refines this location by finding codeword boundaries in a reference codebook. The system then traverses the file to identify codewords corresponding to the queried data. Each codeword has associated arithmetic metadata including numeric values and data types stored in the reference codebook. The system performs arithmetic operations directly on these codewords using their metadata, without decompressing them back to their original form. Results of arithmetic operations are generated as new codewords. This approach enables mathematical computations on compressed data while maintaining the storage efficiency of data compaction.