Cognitive Database System for Cross-Model Data Synchronization
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Solution Overview
Problem
In large organizations, actions driven by disparate groups using interdependent data models often result in incomplete, out-of-sync, and erroneous data due to the lack of consideration for interrelationships between data models, leading to inconsistencies and inaccuracies.
Innovation Solution
A cognitive database system that automatically detects dependencies and relationships between data models through multi-dimensional matching, allowing seamless access and updating of implicated data, generating predictive actions to modify network parameters and resources in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If disparate groups independently manage separate data models, then each group can maintain and update their own data, but data completeness and accuracy deteriorate due to lack of coordination on interrelationships
Solution Approach 1:
The patent introduces an automated data management system that acts as an intermediary between disparate data models. This system automatically detects dependencies between data elements across different groups' data models and propagates updates accordingly, eliminating the need for manual coordination while maintaining data completeness and accuracy.
Solution Approach 2:
The system implements automated feedback mechanisms by continuously monitoring data relationships and automatically propagating updates when changes occur. This feedback loop ensures that all data models remain synchronized without requiring manual intervention, thus maintaining both operational autonomy and data reliability.
2Productivity
If groups update data models at different times without coordination, then each group can work independently, but data synchronicity deteriorates leading to out-of-sync data
Solution Approach 1:
The automated system serves as a mediator that coordinates updates across different groups' data models. It detects when updates occur in one data model and automatically propagates these changes to related data models, ensuring synchronicity is maintained even as groups work independently at different times.
Solution Approach 2:
The system performs preliminary actions by pre-establishing dependency relationships between data elements before updates occur. This allows the system to automatically determine which data models need to be updated and propagate changes in the correct sequence, maintaining synchronicity without requiring coordinated scheduling.
3Productivity
If interrelationships between data models are not considered, then data management is simpler and faster, but data accuracy deteriorates due to erroneous data propagation
Solution Approach 1:
The system implements self-service by automatically detecting and managing data relationships without requiring manual specification. It autonomously identifies dependencies between data elements and propagates updates only where appropriate, maintaining high data accuracy while preserving fast update speeds through automation.
Solution Approach 2:
The patent replaces manual mechanical processes of tracking and propagating data relationships with an automated computational system. This substitution uses algorithms to detect dependencies and propagate updates, eliminating human error while maintaining operational efficiency.
Data Source
AI summary
A cognitive database system matches a particular data element, that is specified as part of received user input, to a first set of data elements in a set of data models based on semantic commonality, and matches the particular data element to a second set of data elements in the set of data models based on visual commonality between graphical representations of the particular data element and the second set of data elements. The cognitive database may determine interdependency between the particular data element and a particular subset of the first and second sets of data elements based on a measure of semantic commonality and/or visual commonality, and may generate a response to the user input with values from the particular subset of data elements that are updated directly from the interdependency that is determined upon receiving the user input.