Adaptive Data Compression Technique Selection
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Solution Overview
Problem
Selecting an optimal compression technique for data in a database system is challenging due to the large number of possible combinations of standalone and combined techniques, requiring a method to determine the best approach based on demographics and access efficiency.
Innovation Solution
A method that determines demographics for data, calculates compression ratios and access efficiency for each technique, ranks them, and selects the best technique for compression, considering both size reduction and access efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple compression techniques are combined to form complex techniques, then compression capability is improved, but the number of possible techniques increases making selection difficult
Solution Approach 1:
The patent segments the compression technique selection process into distinct components: demographics determination, compression ratio calculation, access efficiency determination, and ranking. This segmentation allows the system to handle the complexity of multiple techniques by breaking down the selection process into manageable steps, each focusing on a specific aspect of compression performance.
Solution Approach 2:
The patent changes the parameters used for evaluation by introducing demographics-based predictions for compression ratio and access efficiency. Instead of evaluating techniques based on fixed metrics, the system dynamically adjusts evaluation parameters based on the specific characteristics of the data being compressed, enabling optimal technique selection from the large space of possibilities.
2Quantity of substance
If compression ratio is maximized, then storage efficiency is improved, but access efficiency may deteriorate
Solution Approach 1:
The patent applies dynamics by making the compression technique selection adaptive rather than static. The system dynamically selects compression techniques based on real-time evaluation of demographics, compression ratio, and access efficiency. This allows the system to optimize for storage efficiency when data is written, while maintaining fast access when data is read, by adjusting the compression strategy based on current operational conditions.
Solution Approach 2:
The patent implements feedback mechanisms by evaluating access efficiency as part of the compression technique selection process. The system uses feedback from access patterns and demographics to adjust compression strategy, ensuring that highly compressed data can still be accessed efficiently. This feedback loop allows continuous optimization of both storage efficiency and access performance.
Data Source
AI summary
Demographics for data are determined. A compression ratio (“CR”) is determined for each of a plurality of compression techniques. CR is a size of the data before compression divided into a predicted size of the data after compression. The predicted size of the data after compression is determined as a function of the determined demographics. An access efficiency of each of the compression techniques is determined as a function of the determined demographics. The compression techniques are ranked by CR and access efficiency. A compression technique is selected based on the ranking. The data is compressed using the selected compression technique. The compressed data is stored.