Meta-database for AI Variable Selection
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Solution Overview
Problem
AI systems face challenges in identifying key variables and associated data across multiple, distinct databases, leading to increased processing time, power consumption, and difficulty in modeling accuracy due to scattered data storage.
Innovation Solution
A meta-database system that uses AI algorithms to scan, compress, and profile data from multiple sources, generating granular data types, identifying important variables, and determining associations and probability distributions, allowing for efficient data selection and modeling.
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 increases
Solution Approach 1:
The system performs preliminary actions by scanning, profiling, and creating a compressed meta-database representation of data sources before the actual AI modeling process. This pre-processing identifies key variables, data types, and associations in advance, so that when the AI system needs data, it can access only the relevant portions rather than processing entire data sets, thus maintaining accuracy while reducing processing time.
Solution Approach 2:
The system extracts and isolates key variables and important data elements from large data sets into a compressed meta-database structure. By taking out only the essential variables and their associations rather than processing all raw data, the system maintains modeling accuracy while significantly reducing the amount of data that needs to be processed during AI operations.
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 key variables and important data elements from large data sets into a compressed meta-database structure. By taking out only the essential variables and their associations rather than processing all raw data, the system maintains modeling accuracy while significantly reducing the amount of data that needs to be processed during AI operations, thereby reducing computing power consumption.
Solution Approach 2:
The system performs preliminary actions by scanning, profiling, and creating a compressed meta-database representation of data sources before the actual AI modeling process. This pre-processing identifies key variables, data types, and associations in advance, so that when the AI system needs data, it can access only the relevant portions rather than processing entire data sets, thus maintaining accuracy while reducing computing power consumption.
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 decreases
Solution Approach 1:
The meta-database system serves multiple functions: it stores compressed representations of data from multiple separate sources, profiles data types and variables, identifies key variables and associations, and provides a unified interface for searching and accessing data. This universal system maintains the benefits of distributed storage while eliminating the difficulties of locating relevant data across multiple locations.
Solution Approach 2:
The meta-database acts as an intermediary layer between the separate source databases and the AI system. It profiles and compresses data from multiple sources, identifies key variables and their associations, and provides a unified access interface. This intermediary maintains the data in separate physical locations while making it easy to locate and access relevant data through the meta-database's profiling and search capabilities.
4Measurement precision
If a user manually identifies important data variables, then the modeling accuracy can be improved, but the time and effort required increases
Solution Approach 1:
The system performs self-service by automatically scanning data sources, profiling data types, identifying key variables, and determining associations between variables without requiring manual user intervention. The AI-driven meta-database system autonomously identifies important data variables and prepares them for modeling, thereby maintaining high modeling accuracy while eliminating the time and effort that would be required for manual variable identification.
Data Source
AI summary
A system for maintaining a meta-database including meta-data representing decentralized data from source databases, which cause inefficient selection of modeling data and/or variables. Each of source and meta-data interfaces communicate with the respective database(s). 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 source data from the source interface, compresses the data, and synchronizes the data with the meta-data using the meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines variables indicative of the meta-data, generates variable probability distributions, produces 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 variables, variable probability distributions, and/or variable associations to more efficiency select modeling data and/or variables.