JSON Normalization via Relative Path Entity Mapping
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Solution Overview
Problem
Current methods for transforming semi-structured data, such as JSON, into a structured format for analytical, transformation, or reporting tools require custom logic, which lacks reusability and becomes costly and effort-intensive due to changes in JSON structure, and there is no common approach to make this data relational and useful.
Innovation Solution
A system and method that normalizes a JSON structure by receiving input defining entity types of a target data structure, traversing the source JSON structure to map values to corresponding entities using relative paths, and outputting a structured data format, allowing for user configuration and handling various JSON data types without dependency on the JSON structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom logic is written to convert semi-structured JSON data into structured format, then transformation capability is achieved, but reusability is poor and modification cost is high when JSON structure changes
Solution Approach 1:
The patent creates a universal transformation framework that can handle multiple JSON structures through a common entity type system. The same transformation engine can process different JSON formats by simply changing the entity type definitions, making the system multi-functional and highly reusable across different data structures without requiring custom logic for each case
Solution Approach 2:
The patent uses configurable entity types with adjustable properties and relationships as parameters. When the JSON structure changes, users can modify the entity type definitions (parameters) rather than rewriting the transformation logic itself. This allows the system to adapt to different JSON structures by changing configuration parameters rather than code
2Ease of operation
If tools extract part of JSON or expand JSON as flat name value pairs, then data extraction is simplified, but relational information is lost and data becomes less useful
Solution Approach 1:
The patent introduces entity types as an intermediary layer between the flat JSON structure and the desired relational output. Entity types act as templates that define how flat name-value pairs should be organized into structured relationships. This intermediary preserves relational information by explicitly defining entities, attributes, and relationships while still working with the simple flat structure of JSON data
Solution Approach 2:
The patent segments the JSON data extraction process into distinct components: entity identification, attribute extraction, and relationship definition. By breaking down the transformation into these segments, the system can maintain simplicity in data extraction while systematically rebuilding relational structures through defined entity types and their relationships
3Adaptability or versatility
If no common approach is used to parse JSON and extract data as per user configuration, then flexibility is high, but implementation complexity increases and reusability decreases
Solution Approach 1:
The patent implements a universal configuration system where entity types serve as reusable templates that can be applied to different JSON structures. This universal approach allows users to configure data extraction through standardized entity type definitions rather than writing custom parsing logic for each case, reducing implementation complexity while maintaining flexibility
Solution Approach 2:
The patent performs preliminary action by pre-defining entity types with their attributes and relationships before the actual JSON transformation occurs. This upfront configuration creates a blueprint that guides the transformation process, eliminating the need for complex runtime decision-making and reducing implementation complexity while preserving user flexibility through customizable entity type definitions
Data Source
AI summary
As described herein, a system, method, and computer program are provided for normalizing a JSON structure. In use, input defining at least one entity type of a target data structure is received. A source JSON structure is identified. The source JSON structure is traversed for a particular JSON data type to map values in the source JSON structure to corresponding entities of the target data structure based on the at least one entity type defined for the target data structure, where each entity of the target data structure is defined using a relative path between nodes of the source JSON structure.