Meta-Database Model Training Across Disparate Data Sources
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Solution Overview
Problem
Existing AI systems face challenges in identifying relevant data across multiple, distinct databases for accurate modeling, leading to increased processing time, power consumption, and reduced efficiency due to the difficulty in locating and utilizing important variables and data stored in separate locations.
Innovation Solution
A meta-database system that utilizes AI algorithms to scan, categorize, and profile data from multiple sources, identifying key variables, generating associations, and determining probability distributions to facilitate efficient model training using a subset of relevant data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional input data is provided to an AI system, then the accuracy of the generated output is improved, but the processing time and algorithm training time increase
Solution Approach 1:
The system performs preliminary actions by creating a meta-database that pre-identifies, catalogs, and organizes relevant variables and data sources before the actual AI modeling process. This advance preparation enables rapid retrieval of relevant data during model training, reducing the time penalty associated with using large datasets while maintaining accuracy.
Solution Approach 2:
The meta-database serves as an intermediary layer between the raw data sources and the AI system. It pre-processes and structures information about available data, including variable relationships and relevance metadata, allowing the AI system to efficiently access only the most relevant data without manually searching through extensive storage architectures.
2Measurement precision
If additional input data is provided to an AI system, then the accuracy of the generated output is improved, but the computing power consumption increases
Solution Approach 1:
The system extracts and isolates only the most relevant variables and data elements needed for accurate modeling from the extensive data storage architecture. The meta-database identifies and extracts key variables with their relationships, allowing the AI system to process a focused subset of data that maintains accuracy while reducing computing power consumption.
Solution Approach 2:
The system changes parameters by transforming raw data into structured metadata that includes information about variable relevance, relationships, and data quality. This parameter transformation enables the AI system to efficiently select and process only the necessary data subset, reducing energy consumption while preserving modeling accuracy.
3Quantity of substance
If data is stored in distinct and separate digital locations, then the data storage capacity is increased, but the ease of locating and identifying relevant data is reduced
Solution Approach 1:
The meta-database provides a universal interface that can access and describe data across multiple distinct digital locations. It creates a unified view of distributed data sources, maintaining the benefits of distributed storage while providing centralized access and identification capabilities through a single programming interface.
Solution Approach 2:
The system adds a new dimension to data storage by creating a metadata layer that describes and indexes data across multiple physical locations. This additional dimensional layer organizes data by variables, relationships, and relevance rather than by physical storage location, making it easy to locate relevant data regardless of where it is physically stored.
4Loss of time
If the user manually identifies important data and variables, then the processing time is reduced, but the difficulty of identifying relevant variables increases
Solution Approach 1:
The system performs self-service by automatically identifying, cataloging, and organizing relevant variables and data relationships in the meta-database without requiring manual user intervention. The automated processes scan source databases, identify key variables, and structure the information, reducing both the time and expertise required for users to locate relevant data.
Solution Approach 2:
The meta-database acts as an intermediary that bridges the gap between raw data and user understanding. It automatically analyzes and structures data relationships, presenting relevant variables and their connections in an organized manner that reduces the difficulty of identification while enabling rapid access to important data.
Data Source
AI summary
A system for training a model from a subset of data representing decentrally stored source databases. A key variable repository module operably couples the databases and includes an AI program with a scanner algorithm and a profiler algorithm. The scanner algorithm receives the training data from a source interface, compresses the training data, and synchronizes the training data with the meta-data using a meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines training variables indicative of the meta-data, generates variable probability distributions, produces training variable associations, and modifies the meta-database to include the probability distributions and associations using the meta-data interface. The key interface allows for searching the meta-database for training variables, variable probability distributions, and/or variable associations. A model of the system may be trained in less time with a subset of data associated with the training variable.