Mapping Language for Deep Structured Data Structures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data processing technologies face challenges in efficiently mapping deep structured data structures between different formats, requiring manual selection and navigation through hierarchical nodes, which is cumbersome and lacks explicit control flow for dynamic and unbounded data.
Innovation Solution
A mapping language is introduced that uses statements comprising source and target selections with assignment operations, allowing for iterative and nested mappings, supporting navigation up and down hierarchy levels, and enabling filtering, projections, and transformations, similar to a map and reduce approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual selection and navigation through hierarchical nodes is used for mapping data structures, then flexibility in handling different formats is improved, but operation complexity and time consumption increase significantly
Solution Approach 1:
The patent introduces an automated mapping system that acts as an intermediary between source and target data structures. This system uses mapping templates and algorithms to automatically match nodes between different hierarchical formats, eliminating the need for manual node-by-node selection while maintaining flexibility through configurable mapping rules and support for multiple data formats.
Solution Approach 2:
The mapping system performs self-service by automatically navigating through hierarchical nodes and selecting corresponding elements between source and target structures. The algorithm independently evaluates node relationships, determines mapping relationships, and executes the transformation without human intervention, thereby reducing operational complexity while preserving adaptability through programmable mapping logic.
2Manufacturing precision
If manual mapping of deep structured data structures is performed, then precision in node selection can be maintained, but time consumption and productivity decrease
Solution Approach 1:
The patent implements preliminary action by pre-defining mapping templates and validation rules that encode precision requirements for node selection. These templates are prepared in advance and contain the logic for identifying corresponding nodes between different data structures, allowing the system to quickly and accurately perform mappings without manual intervention while maintaining high precision through the pre-configured mapping criteria.
Solution Approach 2:
The system uses parameter changes by transforming manual precision-based node selection into automated algorithmic processing with configurable parameters. The mapping algorithm accepts parameters such as node depth, element types, and relationship patterns to automatically identify corresponding nodes, thereby maintaining selection precision while dramatically reducing time consumption through computational efficiency.
3Productivity
If automated mapping systems are introduced to reduce manual effort, then productivity increases, but control over the mapping process and debugging capability may be reduced
Solution Approach 1:
The patent incorporates feedback mechanisms that provide detailed information about the automated mapping process. The system generates logs and reports showing which nodes were mapped, the mapping relationships established, and any errors encountered. This feedback enables operators to debug and validate the mapping process efficiently while maintaining high productivity through automation, as the system transparently reports its actions and decisions.
4Adaptability or versatility
If complex hierarchical navigation is performed manually through different data structure formats, then adaptability to various document formats is maintained, but loss of time increases
Solution Approach 1:
The patent implements universality by creating a format-agnostic mapping system that can handle multiple document formats (XML, JSON, YAML, etc.) through a unified approach. The system uses universal mapping templates and algorithms that work across different hierarchical structures, eliminating the need for format-specific manual navigation while maintaining adaptability through configurable mapping rules that can be adjusted for different document types.
Data Source
AI summary
Methods and apparatus, including computer program products, for mapping deep structured data structures. Statements defining a mapping of source elements formatted in accordance with a first hierarchical structure to a target formatted in accordance with a second hierarchical structure are received. The first and second hierarchical structures may be different. A mapping of the source elements to the target in accordance with the statements may be performed, where the statement may be defined in accordance with a mapping language. The mapping language may define that a single statement may represent an iterative approach to mapping elements from the source to the target. The mapping language may support selection of source elements using a format that allows for navigation through a hierarchy of the source. The mapping language may also support nested statements which may allow for nested iterations in which to perform mappings.