Multimodal AI Database Development Guidance from Live Usage
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Solution Overview
Problem
Conventional database development relies on static data, failing to provide guidance for adjusting applications and features based on current usage and multi-modal correlations, leading to potential architectural issues and performance problems.
Innovation Solution
A machine learning model generates recommendations using both static and dynamic data, streaming real-time usage statistics from active databases to optimize database design and development, supporting non-specialist users in designing architectures based on current needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional machine learning models use only static training data, then model training is simple and fast, but the model cannot provide guidance based on current usage and multi-modal correlations
Solution Approach 1:
The patent transforms the static machine learning model into a dynamic system by continuously streaming real-time database usage data from production environments. The model evolves from using only historical static data to incorporating live dynamic data streams, enabling it to adapt to current usage patterns and provide timely recommendations while maintaining training efficiency through incremental learning approaches.
Solution Approach 2:
The system implements a feedback loop where real-time database usage data is continuously collected from production systems, fed back into the machine learning model, and used to update recommendations. This closed-loop feedback mechanism enables the model to learn from actual usage patterns and improve its guidance capability without requiring complete retraining, thus balancing adaptability with training efficiency.
2Reliability
If database development requires specialist skillset and proactive addressing of multiple factors, then database architecture is robust, but development complexity and time increase significantly
Solution Approach 1:
The patent enables non-specialist users to design and optimize database architectures by providing an automated machine learning-based recommendation system. The system self-evaluates usage patterns and generates optimization recommendations without requiring user expertise in database internals, thereby maintaining architecture robustness while reducing development complexity and enabling self-service database development.
Solution Approach 2:
The machine learning model acts as an intermediary between non-specialist users and complex database architecture decisions. It translates high-level usage patterns and requirements into specific architectural recommendations, shielding users from the complexity of database technology choices while ensuring robust architecture through data-driven insights.
3Adaptability or versatility
If database development is custom-built based on specific requirements, then database meets specific needs, but development time and resource requirements increase
Solution Approach 1:
The system performs preliminary analysis of database usage patterns and performance characteristics by continuously monitoring production databases. It pre-computes optimization recommendations based on observed patterns, so when customization is needed, the system can quickly provide tailored recommendations without starting from scratch, thus maintaining adaptability while reducing development time.
Solution Approach 2:
The patent leverages patterns and solutions learned from existing production databases to inform customization of new databases. By copying successful patterns and configurations from proven databases and adapting them to new requirements, the system reduces development time while maintaining adaptability to specific needs through the machine learning-based recommendation engine.
Data Source
AI summary
A method or system for generating recommendations based on detected correlations of static and dynamic data. For example, the system generates feature inputs for the machine learning model that is based on labeled static data as well as dynamic data indicating current usage. Notably, the dynamic data is not retrieved from a data store of known training data, but instead is streamed in real time from active databases accessible to the network. Furthermore, the system provides a formatting mechanism that translates user selected requirements and parameters from a human-readable format indicating particular attributes of a database to optimize (e.g., database performance, security, compliance, capacity planning, etc.) into data that may be included in a machine learning feature input that specifies which correlations to use (e.g., server operational statistics data, server log data, monitoring metrics, etc.).