Metadata-Driven Data Operations Through Parent–Child Models
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Solution Overview
Problem
Organizations face challenges in understanding and efficiently processing large and complex data collections due to inefficient storage methods, which make it difficult for users to access and apply operations on data, especially for non-technical users, and changes to data can break existing data processing operations.
Innovation Solution
A metadata management system uses a metadata model with parent and child nodes to identify and apply operations on data items, automatically determining and applying operations based on metadata relationships, enabling continuous updating and adaptation to data changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If data is stored in a way that facilitates efficient use of computing resources, then computing resource efficiency is improved, but human understanding and accessibility of the data deteriorates
Solution Approach 1:
The patent introduces metadata as an intermediary layer between the efficient computing resource storage format and human users. The metadata management system generates, stores, and utilizes metadata to bridge the gap, enabling non-technical users to understand and access data without requiring changes to the underlying efficient storage structure. This mediator approach allows both computing efficiency and human accessibility to coexist.
2Manufacturing precision
If manual code generation is required to perform operations on data, then operational precision is improved, but time consumption and complexity increase
Solution Approach 1:
The metadata management system enables self-service operation determination by automatically generating operation recommendations based on metadata analysis. Instead of requiring users to manually write code, the system autonomously analyzes the metadata model, identifies appropriate operations, and generates executable code or operation specifications. This self-service mechanism maintains operational precision while dramatically reducing time consumption and complexity for users.
3Reliability
If data processing operations are hardcoded, then operational reliability is improved, but adaptability to data changes deteriorates
Solution Approach 1:
The patent implements dynamic operation determination by linking operations to the metadata model rather than hardcoding them. When the metadata model changes to reflect data structure changes, the operation recommendations automatically adapt through metadata-driven code generation. This dynamic approach maintains reliability by ensuring operations remain consistent with current data structures while simultaneously improving adaptability to changes, eliminating the need to manually update hardcoded operations.
4Measurement precision
If operations are defined at the data item level, then operational specificity is improved, but scalability across multiple data items deteriorates
Solution Approach 1:
The metadata management system implements universality by defining operations at the metadata model level rather than individual data item level. The metadata model serves as a universal template that can be applied across multiple data items with similar structures. When operations are determined based on metadata patterns in the model, they automatically scale to multiple data items while maintaining operational specificity through the detailed metadata definitions. This multi-functional approach allows a single operation definition to serve multiple data items consistently.
Data Source
AI summary
A method for using a metadata model to perform operations on data items, with the metadata model including parent nodes and child nodes connected by edges, with the parent nodes specifying logical metadata and the child nodes specifying physical metadata representing the data items, and with the edges specifying relationships between the nodes. The method includes: identifying a given data item and physical metadata of that given data item, accessing the metadata model, identifying, in the metadata model, a child node representing the physical metadata of the given data item, traversing one or more edges in the metadata model to identify parent nodes of the child node, determining, from logical metadata associated with the identified parent nodes, one or more operations to be performed on the given data item, applying the one or more operations to the given data item to transform the data item, and storing the transformed data item.