AI Mapping Flattened Data to Hierarchical Structures
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Solution Overview
Problem
Current methods lack automation for converting flattened data structures to nested hierarchical structures, leading to time-consuming and error-prone manual processes, especially for large datasets, which hinders efficient data storage and security.
Innovation Solution
A system and method utilizing AI-based mapping to convert flattened schemas to hierarchical schemas, involving user inputs, AI-driven node mapping, and a UI representation to automate the conversion process, reducing manual intervention and enhancing data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual conversion process is used to convert flattened schema to hierarchical schema, then flexibility and control are maintained, but time consumption and human error increase significantly
Solution Approach 1:
The system enables self-service automated conversion by allowing users to input only minimal identifiers, after which the AI automatically performs the complete schema conversion process without requiring manual field-by-field mapping, thus reducing both time consumption and human error
Solution Approach 2:
The patent replaces the manual mechanical process of schema conversion with an AI-based automated system that uses machine learning models to infer and map relationships between flattened and hierarchical schemas, significantly improving efficiency and accuracy
2Adaptability or versatility
If manual conversion process is used for large datasets with thousands of fields, then detailed control over each field is possible, but the process becomes impractical and error-prone
Solution Approach 1:
The system handles large datasets with thousands of fields automatically by requiring minimal user input (identifiers only), enabling the AI to process and map all fields without manual intervention, making the system both adaptable to large datasets and easy to operate
Solution Approach 2:
The AI-based conversion system is designed to handle datasets of any scale uniformly, whether small or large, by using the same automated mapping mechanisms, thus providing universal adaptability across different dataset sizes without compromising operational simplicity
3Ease of manufacture
If flattened structure is used for data storage, then simplicity and ease of storage are achieved, but data security and relationship preservation are compromised
Solution Approach 1:
Instead of attempting to add complexity to flattened structures to achieve security and relationship preservation, the patent inverts the approach by converting data into hierarchical structures that inherently provide these properties through their nested nature, while maintaining ease of storage
Data Source
AI summary
A method and a system for mapping a flattened data structure into a relation preserving data structure is disclosed. The method includes receiving a flattened schema comprising a plurality of columns. The method includes receiving a plurality of user inputs from a user. The plurality of user inputs comprises a plurality of identifiers. The method includes defining a sample target hierarchical schema based on user inputs. The method includes preparing a nested hierarchical structure corresponding to the sample target hierarchical schema in a User Interface (UI) representation. The nested hierarchical structure comprises a plurality of nodes with corresponding identifiers and relationships of the plurality of nodes. The method includes performing an Artificial Intelligence (AI) based mapping of columns of the flattened schema to respective nodes of the plurality of nodes of the nested hierarchical structure. The method further includes generating a target hierarchical schema based on AI based mapping.