Configurable Data Compression Pipelines Across Multiple Data Domains
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data compression algorithms are domain-specific and require multiple algorithms for different data structures, leading to resource-intensive management and sub-optimal performance when used across domains, and determining the appropriate algorithm for a given data set can be challenging.
Innovation Solution
A configurable data compressor/decompressor system that uses a shared registry of compression/decompression operators and recipes, enabling efficient compression and decompression across multiple data domains through offline, online, and hybrid training methods to determine optimal compression pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple domain-specific compression algorithms are maintained for different data domains, then compression performance for each specific domain is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent implements a universal compression system that can handle multiple data domains (textual data, log data, time-series data, etc.) through a single configurable compressor. The system uses a shared registry of compression operators and dynamically configures pipelines based on the input data characteristics, eliminating the need to maintain separate domain-specific compression algorithms while achieving good compression performance across all domains.
2Manufacturing precision
If multiple domain-specific compression algorithms are maintained for different data domains, then compression performance for each specific domain is improved, but resource consumption and maintenance costs increase
Solution Approach 1:
The patent merges multiple domain-specific compression algorithms into a single unified system. The configurable compressor consolidates compression operators, transformation functions, and encoding mechanisms into one resource, sharing common components across all data domains. This reduces memory usage, processing overhead, and maintenance costs compared to maintaining separate compression algorithms for each domain.
3Manufacturing precision
If domain-specific compression algorithms are used for different data structures, then compression effectiveness for each domain is improved, but the difficulty of determining the appropriate algorithm for a given data set increases
Solution Approach 1:
The configurable compression system automatically detects the characteristics of the input data and self-configures the appropriate compression pipeline without requiring manual intervention. The system analyzes data properties (such as data type, structure, and patterns) and dynamically selects and configures the most suitable compression operators and transformations from the shared registry, making the algorithm selection process transparent and automated.
4Device complexity
If a single configurable compression system is used across multiple data domains, then system complexity and resource requirements are reduced, but compression performance may be sub-optimal compared to domain-specific algorithms
Solution Approach 1:
The configurable compression system dynamically adapts its behavior based on the input data characteristics. Rather than using a fixed compression algorithm, the system dynamically configures compression pipelines by selecting and combining appropriate operators from the shared registry based on the specific data domain and characteristics being processed, achieving domain-specific optimization within a unified system.
Solution Approach 2:
The system applies different compression strategies and operators to different parts of the data processing pipeline based on local data characteristics. Each compression operator in the registry is optimized for specific data types or patterns, and the system selectively applies the most appropriate operators to specific data domains, ensuring optimal compression performance for each local context while maintaining a unified system architecture.
Data Source
AI summary
A data service implements a configurable data compressor/decompressor using a recipe generated for a particular data set type and using compression operators of a common registry (e.g., pantry) that are referenced by the recipe, wherein the recipe indicates at which nodes of a compression graph respective ones of the compression operators of the registry are to be implemented. The configurable data compressor/decompressor provides a customizable framework for compressing data sets of different types (e.g., belonging to different data domains) using a common compressor/decompressor implemented using a common set of compression operators.