Multidimensional Data Compression Using Dimension Setting Information
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Solution Overview
Problem
The increasing complexity and dimensionality of IoT data require more storage capacity and efficient compression methods, as existing technologies struggle to generate optimal compressors for multidimensional data, leading to low compression ratios and increased storage needs.
Innovation Solution
A storage system that utilizes dimension setting information to generate compressors, allowing for optimal compression irrespective of the number of dimensions and format of multidimensional datasets, minimizing information deterioration and bit rate, especially when lossless compression is adopted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multidimensional data dimensions increase, then data amount increases, but storage capacity requirement increases
Solution Approach 1:
The patent changes the parameters of the compression system by generating multiple compressors with different hyperparameter settings. Each compressor is optimized for specific data characteristics, allowing the system to achieve higher compression ratios for multidimensional data without proportionally increasing storage capacity.
Solution Approach 2:
The system dynamically selects the most appropriate compressor from multiple candidates based on the actual data characteristics. This dynamic selection mechanism allows the storage system to adapt to varying data dimensions and formats, maintaining efficient compression ratios while managing storage capacity requirements.
2Adaptability or versatility
If data multidimensionalization advances, then data complexity increases, but compressor generation difficulty increases
Solution Approach 1:
The patent performs preliminary actions by pre-generating multiple compressors with different hyperparameter configurations before actual data compression is needed. This preparation phase includes setting diverse hyperparameters such as compression levels, algorithm types, and dimension-specific parameters, so that when multidimensional data arrives, the system can immediately select and apply the most suitable pre-configured compressor without complex real-time generation.
3Loss of information
If optimal compressor is not generated, then compression ratio decreases, but information loss increases
Solution Approach 1:
The system implements feedback mechanisms by evaluating compression results and using this information to refine compressor selection and parameter settings. The feedback loop allows the system to learn from compression performance across different data types and dimensions, continuously improving both compression ratios and information preservation by selecting or adjusting compressors based on actual outcomes rather than static configurations.
Data Source
AI summary
To generate an optimum compressor irrespective of the number of dimensions and a format of a multidimensional dataset. A storage system refers to dimension setting information, which is information representing an attribute for each of data dimensions of the multidimensional dataset, and generates a compressor based on the dimension setting information.