Compression and Search Process for Data Sets
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Solution Overview
Problem
Current data management systems, such as NoSQL databases, face inefficiencies in both time and space due to the lack of effective data compression and failure to exploit data regularities, particularly in handling large datasets from big data and real-time applications.
Innovation Solution
A compression and search process that transforms raw data into a sequence of symbols, applies multiple compression strategies, and optimizes data structures to achieve efficient storage and retrieval, utilizing techniques like block trees and LA-vector compression to exploit repetitiveness and improve performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional data structures (skip list, B-tree) are used for data management, then time efficiency is improved, but space compression is insufficient
Solution Approach 1:
The patent merges multiple compression strategies (byte-oriented, word-oriented, and context-tree weighting) into a unified data management system. This combination allows the system to achieve both time efficiency through optimized data structures and space compression through multiple complementary compression approaches, resolving the contradiction between speed and space occupancy.
Solution Approach 2:
The system dynamically changes compression parameters by selecting different compression strategies based on data characteristics. It transitions between byte-oriented compression, word-oriented compression, and context-tree weighting approaches, adjusting parameters to optimize both access speed and compression ratio for different data patterns.
2Quantity of substance
If data compression is applied to reduce storage space, then space occupancy is improved, but search operation time increases
Solution Approach 1:
The patent applies preliminary compression actions during data ingestion and organization phases. By pre-compressing data using multiple strategies and organizing it in a structured format, the system prepares data for efficient retrieval without compromising search performance. This preliminary organization enables fast search operations even on compressed data.
Solution Approach 2:
The system implements dynamic compression and decompression capabilities that adapt to search patterns. It can dynamically select which compression strategy to apply or reverse based on the specific search operation needed, ensuring that space efficiency is maintained while minimizing search time through intelligent dynamic adjustments.
3Quantity of substance
If multiple compression strategies are combined, then compression effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent segments the compression system into distinct modular components: byte-oriented compression module, word-oriented compression module, and context-tree weighting module. Each module handles specific compression tasks independently, making the overall complex system manageable through clear segmentation. This modular architecture allows each component to be optimized separately while working together to achieve high compression ratios.
Data Source
AI summary
compression and search process on a data set based on multiple strategies, including: extracting a set of raw data and transforming it into a sequence of symbols, manipulating the sequence by combining data structures and one or more (optionally a multiplicity of) compression strategies to obtain compressed and efficiently searchable data, recording the compressed and efficiently searchable data on storage media.