Automated Database Storage Type Recommendation System
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Solution Overview
Problem
Current methods for selecting and adapting database technology are inefficient and costly, relying heavily on manual expertise and struggling to predict future needs due to changing user behavior and data access patterns, leading to suboptimal performance and scalability issues.
Innovation Solution
A method using machine learning to analyze data access patterns, classify them, and determine if a change in data storage type is necessary by comparing current patterns to previous settings, with a trained classifier and trend analysis to differentiate between temporary fluctuations and consistent changes, allowing for automatic or suggested changes in data storage technology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert knowledge is used to select database technology, then selection accuracy may be improved, but the process becomes costly and time consuming
Solution Approach 1:
The system performs self-service by automatically analyzing data access patterns and recommending optimal database technology without requiring manual expert intervention. The automated analysis engine continuously monitors data patterns and generates recommendations based on learned patterns, eliminating the need for expensive and time-consuming manual expert assessments while maintaining high selection accuracy
Solution Approach 2:
The patent replaces the mechanical system of manual expert analysis with an automated machine learning-based analysis engine. This engine uses supervised learning algorithms to classify data access patterns and recommend appropriate database technologies, substituting human expert mechanics with computational processes that are both faster and scalable
2Measurement precision
If manual expert knowledge is used to select database technology, then selection accuracy may be improved, but the process becomes costly
Solution Approach 1:
The system performs self-service by automatically analyzing data access patterns and recommending optimal database technology without requiring manual expert intervention. The automated analysis engine continuously monitors data patterns and generates recommendations based on learned patterns, eliminating the need for expensive and time-consuming manual expert assessments while maintaining high selection accuracy
Solution Approach 2:
The patent replaces the mechanical system of manual expert analysis with an automated machine learning-based analysis engine. This engine uses supervised learning algorithms to classify data access patterns and recommend appropriate database technologies, substituting human expert mechanics with computational processes that are both faster and scalable
3Stability of the object's composition
If current database technology is used without change, then existing system stability is maintained, but performance becomes suboptimal when data access patterns change
Solution Approach 1:
The system implements dynamic adaptation by continuously monitoring data access patterns and automatically detecting when changes occur that would benefit from different database technology. Rather than static manual reviews, the system dynamically adjusts recommendations based on real-time pattern recognition, maintaining system stability while optimizing performance through automated change detection and recommendation generation
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors data access patterns, compares them against learned patterns, and generates recommendations when deviations indicate suboptimal performance. This closed-loop feedback system ensures that the database technology remains aligned with actual data usage patterns, automatically adapting to maintain both stability and performance
4Measurement precision
If manual analysis of data access patterns is performed, then accurate recommendations can be generated, but the process becomes complex and requires extensive expertise
Solution Approach 1:
The patent replaces complex manual analysis mechanics with automated machine learning algorithms that perform pattern classification. The supervised learning engine automatically learns from labeled data access patterns and applies this knowledge to generate accurate recommendations, eliminating the need for complex manual analysis processes and extensive expert knowledge while maintaining high recommendation accuracy
Solution Approach 2:
The system performs self-service by automatically analyzing data access patterns and recommending optimal database technology without requiring manual expert intervention. The automated analysis engine continuously monitors data patterns and generates recommendations based on learned patterns, eliminating the need for expensive and time-consuming manual expert assessments while maintaining high selection accuracy
Data Source
AI summary
A method is provided for analyzing data storage applied on at least one data storage type. The data to be stored is transmitted in at least one data stream between at least one application instance and the at least one data storage type. The method includes accessing, from the at least one data stream, data selected according to pre-defined rules. Data access patterns are aggregated on the basis of the selected data, where the data access patterns are indicative of the at least one storage type applied. Classifiers are obtained by applying trained classifiers to the aggregated data access patterns. Differences between the obtained classifiers and the trained classifiers are analyzed to determine, that the obtained classifiers are indicative of at least one data storage type other than a presently used data storage type, in case at least one predefined threshold value is exceeded when analyzing the differences.