Supervised Learning Model for Data Compressibility Analysis
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Solution Overview
Problem
Existing data compression methods are resource-intensive and inefficient, as they often attempt to compress data that is already compressed or not further compressible, leading to wasted processing power and resource usage.
Innovation Solution
A computer system employs a supervised learning model, such as an artificial neural network (ANN), to perform pre-compression analysis on data, determining its compressibility and bypassing unnecessary compression steps for non-compressible data while sending compressible data for further compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to all data, then data size is reduced, but processing resources are wasted on already compressed data
Solution Approach 1:
The system performs pre-compression analysis using a trained neural network model to evaluate data compressibility before applying compression algorithms. This preliminary assessment identifies which data streams are suitable for compression, preventing wasted processing resources on already compressed or non-compressible data while ensuring compression is applied only where beneficial.
2Productivity
If compression analysis is performed on all data, then compressible data is identified, but processing time increases
Solution Approach 1:
The system uses a neural network model that was pre-trained on training data streams containing both compressed and uncompressed data patterns. The trained model serves as a copy of compression knowledge that can rapidly evaluate new data streams without requiring actual compression attempts, significantly reducing processing time compared to traditional trial-based compression analysis.
3Quantity of substance
If data is routed through compression always, then compression opportunities are captured, but storage hardware requirements increase due to unnecessary processing
Solution Approach 1:
The system segments the data processing pipeline into distinct paths: a pre-compression analysis path using the neural network model, and conditional routing paths for compressed versus non-compressed data. This segmentation allows the system to apply compression only to suitable data streams, reducing overall storage requirements while maintaining a manageable processing pipeline through intelligent routing decisions.
Data Source
AI summary
Data may be handled based on compressibility (i.e., whether the data may be further compressed or is not further compressible). A supervised learning model may be trained using a set of known further compressible data and a set of known non-compressible data. Using these data sets, the model may generate weighting factors and bias for the particular data sets. The trained model may then be used to evaluate a set of unclassified data.