Dynamic Compression Filters for Patterned Numeric Data
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Solution Overview
Problem
Conventional data compression techniques are suboptimal for compressing patterned numerical data, particularly in the 'big data' era, leading to inefficient storage and retrieval costs and query execution performance.
Innovation Solution
The technology employs a sequence of dynamically selected compression filters that identify and encode common patterns and patterns of patterns in numeric data, using a combination of manually chosen and dynamically chosen filters, including new algorithms like Dictionary Run-Length Encoding and Week Of Encoding, to achieve high compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data compression techniques are used, then data storage is achieved, but compression ratio is insufficient for patterned numerical data
Solution Approach 1:
The patent implements dynamic filter selection where the compression system automatically chooses from multiple candidate filters (including domain-specific filters like temporal-spatial, spectral, and wavelet filters) based on the characteristics of the input data. This dynamic adaptation allows the system to achieve high compression ratios for different types of patterned numerical data without manual configuration, resolving the contradiction between compression effectiveness and system complexity.
Solution Approach 2:
The system changes compression parameters dynamically by selecting different filter types and configurations based on data characteristics. The patent employs a framework that evaluates data patterns and adjusts compression parameters (filter selection, transformation type, encoding method) to optimize compression ratio for the specific data being processed, thereby achieving high compression without requiring complex manual setup.
2Adaptability or versatility
If static compression filter configuration is used, then implementation is simple, but compression performance is suboptimal for different data types
Solution Approach 1:
The patent implements dynamic filter selection where the compression system automatically chooses from multiple candidate filters (including domain-specific filters like temporal-spatial, spectral, and wavelet filters) based on the characteristics of the input data. This dynamic adaptation allows the system to achieve high compression ratios for different types of patterned numerical data without manual configuration, resolving the contradiction between compression effectiveness and system complexity.
Solution Approach 2:
The compression system performs self-service by automatically analyzing data characteristics and selecting appropriate filters without external intervention. The patent's framework includes automated data pattern recognition and filter selection mechanisms that enable the system to adapt to different data types independently, achieving high versatility without proportionally increasing operational complexity.
3Quantity of substance
If higher compression ratios are achieved, then storage costs are reduced, but query execution time may increase due to decompression overhead
Solution Approach 1:
The patent applies preliminary action by pre-processing data through optimal filter selection and compression configuration before storage. The system analyzes data characteristics upfront and applies the most suitable compression filters, ensuring both high compression ratios and efficient decompression. This preliminary optimization reduces the trade-off between compression ratio and query performance by preparing data in advance with the best compression parameters.
Solution Approach 2:
The system changes compression parameters dynamically by selecting different filter types and configurations based on data characteristics. The patent employs a framework that evaluates data patterns and adjusts compression parameters (filter selection, transformation type, encoding method) to optimize compression ratio for the specific data being processed, thereby achieving high compression without requiring complex manual setup.
Data Source
AI summary
Certain implementations of the disclosed technology may include methods and computing systems for performing high-density data compression, particularly on numerical data that demonstrates various patterns, and patterns of patters. According to an example implementation, a method is provided. The method may include extracting a data sample from a data set, compressing the data sample using a first compression filter configuration, and calculating a compression ratio associated with the first compression filter configuration. The method may also include compressing the data sample using a second compression filter configuration and calculating a compression ratio associated with the second compression filter configuration. A particular compression filter configuration to utilize in compressing the entire data set may be selected based on a comparison of the compression ratio associated with the first compression filter configuration and a compression ratio associated with the second compression filter configuration.