Data Compression Service With ML-Based Technique Selection
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Solution Overview
Problem
The challenge lies in efficiently selecting the most appropriate data compression technique for various types of data, as existing methods often require significant resources and are limited by time, operational costs, and other constraints, making it difficult to determine the best compression method for different data formats and increasing storage costs.
Innovation Solution
A data compression service that utilizes a rules-based analysis and machine-learning techniques to select the most efficient compression techniques based on data characteristics and metadata, generating multiple compression candidates while adhering to service restrictions such as time limits or cost caps, and applying multi-level compression to optimize data size and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple compression techniques are analyzed to select the best one, then data compression efficiency is improved, but resource consumption and time requirements increase
Solution Approach 1:
The system performs preliminary analysis of data characteristics and metadata before compression to pre-determine the most suitable compression technique. This advance preparation allows the system to select compression methods based on pre-evaluated data properties, avoiding time-consuming trial-and-error compression attempts during actual compression operations.
Solution Approach 2:
The patent replaces manual or rule-based compression technique selection with machine learning models that automatically analyze data characteristics and predict the most effective compression method. This substitution of mechanical decision-making processes with intelligent algorithms significantly reduces the time required for compression selection while improving accuracy.
2Productivity
If comprehensive data analysis is performed to determine the best compression technique, then compression effectiveness is improved, but operational costs increase
Solution Approach 1:
The system applies different levels of analysis depth to different data types based on their characteristics. For example, simple data types may receive basic analysis while complex data types receive more comprehensive analysis. This localized approach ensures effective compression is achieved where needed while avoiding unnecessary computational expenses for simpler data types.
Solution Approach 2:
The machine learning models dynamically adjust analysis parameters such as the depth of data inspection, the number of compression techniques evaluated, and the complexity of metadata analysis based on data characteristics. This adaptive parameter adjustment allows the system to optimize the balance between compression effectiveness and operational costs for each specific data type.
3Measurement precision
If advanced machine-learning techniques are used to select compression methods, then compression selection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the compression selection process into distinct functional modules: data characteristic analysis, metadata processing, machine learning model evaluation, and compression technique selection. Each module handles a specific aspect of the selection process, making the overall complex system more manageable and maintainable while preserving high selection accuracy.
Solution Approach 2:
The patent introduces intermediary components such as feature extraction layers that transform raw data characteristics into standardized inputs for machine learning models. These intermediaries simplify the interface between complex ML algorithms and the compression selection logic, reducing system complexity while maintaining prediction accuracy.
Data Source
AI summary
Data may be efficiently analyzed and compressed as part of a data compression service. A data compression request may be received from a client indicating data to be compressed. An analysis of the data or metadata associated with the data may be performed. In at least some embodiments, this analysis may be a rules-based analysis. Some embodiments may employ one or more machine learning techniques to historical compression data to update the rules-based analysis. One or more compression techniques may be selected out of a plurality of compression techniques to be applied to the data. Data compression candidates may then be generated according to the selected compression techniques. In some embodiments, a compression service restriction may be enforced. One of the data compression candidates may be selected and sent in a response.