Native Machine Learning Integration in Data Management Products
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Solution Overview
Problem
Data management products lack integrated machine learning capabilities, limiting their ability to provide trained machine learning interfaces and predictions using underlying data, restricting users' access to predictive insights.
Innovation Solution
An integrated system that includes a machine learning module within data management products, enabling the execution of machine learning ensembles to predict unknown values and provide native access to these predictions, allowing users to access machine learning results directly within the data management interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning capabilities are integrated into data management products, then predictive insights and analysis capabilities are enhanced, but device complexity increases
Solution Approach 1:
The patent combines machine learning functionality directly within the data management product architecture, merging predictive analytics capabilities with existing data storage and manipulation functions. This integration allows the system to provide both data management and predictive insights through a unified platform, enhancing versatility while managing complexity through consolidation rather than separate systems.
Solution Approach 2:
The data management product is designed to perform multiple functions: traditional data operations (store, retrieve, manipulate) and machine learning operations (predict, analyze, generate insights). By making the system universal and multi-functional, it can serve diverse analytical needs within a single platform, improving adaptability without requiring entirely separate specialized systems.
2Measurement precision
If machine learning ensembles are executed to predict unknown values, then analysis precision is improved, but loss of time increases due to computation
Solution Approach 1:
The system pre-trains machine learning models and ensembles during off-peak periods or initialization phases, so that when prediction requests are made, the heavy computational training work has already been completed. This allows rapid inference and prediction without requiring extensive computation time at the moment of query, thus maintaining high accuracy while reducing perceived computation time.
Solution Approach 2:
The system dynamically adjusts parameters such as ensemble size, model complexity, and training depth based on available computational resources and time constraints. By changing these parameters adaptively, the system can balance prediction accuracy with computation time, providing high precision when resources permit and faster results when time is constrained.
3Ease of operation
If native machine learning interface is provided within data management product, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary layer or adapter that translates between standard data management operations and machine learning-specific operations. This intermediary handles the complexity of model training, parameter tuning, and ensemble coordination internally, while presenting a simplified, familiar interface to users based on conventional data management concepts, thus improving ease of operation without exposing users to underlying complexity.
Solution Approach 2:
The machine learning components are designed to be self-configuring and self-managing, automatically handling model selection, parameter optimization, and ensemble construction based on the data and user requirements. This self-service capability reduces the need for manual configuration and expert intervention, making the system easier to operate while the underlying complexity is managed autonomously by the system itself.
Data Source
AI summary
Apparatuses, systems, methods, and computer program products are disclosed for machine learning in a data management product. The apparatus includes an input module, a learned function module, and a results module. The input module is configured to receive an analysis request for the data management product. The learned function module is configured to execute one or more machine learning ensembles to predict one or more unknown values for the data management product. The result module is configured to provide native access, within the data management product, to the one or more unknown values.