ML Compression Recommendation Using Dataset Statistical Attributes
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Solution Overview
Problem
Current data compression algorithms vary significantly in compression ratio and time requirements, making it difficult to recommend the optimal algorithm for a given dataset, as no single algorithm can efficiently compress all input files with consistent results.
Innovation Solution
A machine learning-based approach that utilizes statistical attributes such as size, entropy, and chi-square test to identify the best compression algorithm for a dataset by analyzing the behavior of different algorithms and recommending the most suitable one based on objectives like storage savings or compression time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single compression algorithm is used for all datasets, then the system is simple and easy to operate, but the compression ratio and time efficiency vary significantly across different data types
Solution Approach 1:
The patent segments the compression process by dividing datasets into different categories based on statistical attributes (e.g., text, binary, compressed, encrypted). A classification system analyzes dataset characteristics and routes them to appropriate compression algorithms, thereby achieving both operational simplicity through automation and high compression efficiency through specialized algorithms for each data type.
Solution Approach 2:
The patent changes the parameters of the compression system by introducing statistical analysis parameters (entropy, chi-square test results, data size) to dynamically select compression algorithms. This allows the system to adapt to different data characteristics automatically, maintaining ease of operation while optimizing compression performance for each specific dataset type.
2Productivity
If multiple compression algorithms are tested on each dataset, then the optimal algorithm can be identified, but the time and computational resources required increase significantly
Solution Approach 1:
The patent applies preliminary action by performing statistical analysis (calculating entropy, chi-square tests, and other attributes) on datasets before actual compression. This pre-analysis quickly categorizes the data type and predicts the best compression algorithm, avoiding the need to test multiple algorithms on every dataset and significantly reducing the time required for algorithm selection.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between the dataset and compression algorithms. This intermediary analyzes statistical attributes and recommends the optimal algorithm based on trained models, eliminating the need for exhaustive algorithm testing while ensuring optimal compression performance is achieved.
3Measurement precision
If compression algorithms are selected based on statistical attributes, then accurate predictions of output size and time can be made, but the complexity of the system increases
Solution Approach 1:
The patent implements self-service by enabling the compression system to automatically analyze statistical attributes, train classification models, and select optimal algorithms without external intervention. The system uses the datasets themselves to train the classification model, allowing it to improve its prediction accuracy over time while maintaining manageable complexity through automated processes.
Data Source
AI summary
Described herein is a system that facilitates recommending data compression using machine learning and statistical attributes. According to an embodiment, a system can comprise receiving a dataset, statistical attributes associated with the dataset, and a compression requirement for compression of the dataset. The system can further comprise based on the statistical attributes and the compression requirement, estimating a first compression attribute and a second compression attribute of a group of compression processes. The system can further comprise selecting a primary compression process from the group of compression processes, based on an output of an analytics component, wherein the analytics component employs a neural network to determine the primary compression process based on analysis of the statistical attributes, the compression requirement, and a compression objective.