Time Series Difference Compression for Lossless Wide-Range Data
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Solution Overview
Problem
Existing methods for compressing time series data are not suitable for lossless compression, particularly for datasets with a wide range of values, as they either require obtaining original raw data or are not applicable to time series data, leading to inefficiencies in storage and transmission.
Innovation Solution
The apparatus generates difference data by comparing reference data with actual measurement data, reducing the data volume, and then applies lossless compression techniques like run length encoding or dictionary encoding to the difference data for efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression is applied to time series data with wide value ranges, then data integrity is maintained, but compression effectiveness is poor
Solution Approach 1:
The patent transforms the data representation by calculating differences between consecutive time series values. This parameter transformation converts absolute values with wide ranges into difference values with smaller ranges, enabling effective lossless compression while maintaining the ability to reconstruct original data through reverse calculation.
2Device complexity
If conventional compression methods are used on time series data, then processing is simpler, but compression ratio is insufficient
Solution Approach 1:
The patent applies a preliminary transformation step before compression by calculating difference values between consecutive time series points. This preprocessing action creates a transformed dataset with better compression characteristics, allowing conventional lossless compression algorithms to achieve superior compression ratios without increasing operational complexity.
3Volume of stationary object
If difference data is generated and compressed, then storage volume is reduced, but computational cost increases
Solution Approach 1:
The patent transforms the data representation by calculating differences between consecutive time series values. This parameter transformation converts absolute values with wide ranges into difference values with smaller ranges, enabling effective lossless compression while maintaining the ability to reconstruct original data through reverse calculation.
Data Source
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AI summary
The present disclosure relates to an apparatus for compressing time series data comprising: a difference detection unit 30 configured to determine difference data from reference data and measurement data, a data compression unit 40 configured to compress the difference data determined by the difference detection unit 30, a data export 50 unit configured to provide the compressed difference data to a data storage device.