Adaptive Delta Compression for Variable Timeseries Patterns
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Solution Overview
Problem
Existing data compression systems, particularly dictionary compression, are inefficient and slow when handling timeseries data, failing to keep pace with fast storage devices and often degenerating under variable inputs, which hampers data compression speed and effectiveness.
Innovation Solution
The implementation of an adaptive delta compression technique, Delta4C, which leverages statistical analysis to select the most efficient compression strategy among LZ4, delta, and delta-delta methods, dynamically adjusting based on data patterns and properties to optimize compression speed and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dictionary compression is used for timeseries data, then compression can be performed with a dictionary of frequent data pieces, but the system becomes slow and impractical compared to fast storage devices
Solution Approach 1:
The patent changes the compression approach from dictionary-based to delta-based compression specifically optimized for timeseries data. By computing deltas (differences) between consecutive data points and applying statistical analysis to determine optimal compression parameters, the system achieves faster compression speeds that can keep pace with modern storage devices while maintaining effectiveness for timeseries patterns.
Solution Approach 2:
The patent implements adaptive delta compression that dynamically adjusts compression parameters based on the statistical properties of the input data. The system performs statistical analysis to compute average delta and adapts its compression strategy in real-time, allowing it to respond to varying data patterns and maintain high compression speeds across different timeseries characteristics.
2Productivity
If dictionary compression is used, then frequent data pieces can be referenced efficiently, but the system has highly variable speed and degenerates on non-frequent inputs
Solution Approach 1:
The system changes from fixed dictionary compression to adaptive delta compression where parameters are dynamically determined through statistical analysis of the input data. This allows consistent performance across different data types by adjusting compression parameters based on observed statistical properties rather than relying on pre-built dictionaries that may not match the input data characteristics.
Solution Approach 2:
The patent performs preliminary statistical analysis on the input timeseries data before applying compression. By computing statistics such as average delta and analyzing data patterns in advance, the system prepares optimal compression parameters that ensure consistent and reliable compression performance regardless of the specific input data characteristics.
3Device complexity
If traditional compression methods are used, then simple compression can be applied, but they fail to recognize patterns like iota pattern and achieve optimal compression ratios
Solution Approach 1:
The system performs preliminary statistical analysis to identify patterns in the timeseries data before compression. By computing average delta and analyzing data characteristics in advance, the system recognizes patterns such as the iota pattern and other timeseries-specific structures, enabling it to apply appropriate compression strategies that achieve superior compression ratios.
Solution Approach 2:
The patent employs adaptive parameter selection based on statistical analysis results. Rather than using a fixed simple compression method, the system adjusts compression parameters and selects from multiple compression strategies (including delta, delta-delta, and other specialized methods) based on the observed data patterns, achieving optimal compression ratios for different timeseries characteristics.
Data Source
AI summary
Example systems and methods that perform adaptive delta compression for timeseries data are described. In one implementation, one or more computer processors analyze timeseries data to identify properties about the data. The one or more computer processors select at least one compression primitive based on at least one test. A window size is dynamically changed based on the analysis results and the at least one compression primitive.