XML to Dynamic Language Data Expression Mapping
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Solution Overview
Problem
Computing systems face challenges in intercommunicating due to incompatible message formats, such as XML and dynamic language expressions like JSON, which differ in structure, ordering, and type assignments, leading to difficulties in data interchange and interoperability.
Innovation Solution
The method involves mapping between XML and dynamic language data expressions, using specialized serialization stacks that can interpret both formats, preserving data organization and type information, and allowing for data to be translated between these formats while maintaining compatibility, enabling standard data handling and infrastructure minimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If XML and dynamic language expressions are used for inter-computer system communication, then compatibility and interoperability are improved, but data structure compatibility and information preservation deteriorate due to format differences
Solution Approach 1:
The patent introduces an intermediary mapping layer that translates between XML and dynamic language expressions. This mediator preserves the original data structure and organization while enabling communication between systems using different formats, thus resolving the contradiction between compatibility and information preservation
Solution Approach 2:
The system dynamically adjusts data representation parameters based on the target format. When converting between XML and dynamic language expressions, the mapping mechanism changes structural parameters (ordering, root names, type assignments) while preserving semantic information, enabling both compatibility and information fidelity
2Adaptability or versatility
If specialized serialization stacks are used to interpret both XML and dynamic language expressions, then interoperability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal serialization stack that can handle both XML and dynamic language expressions through a single unified architecture. This multi-functional approach improves interoperability while avoiding the need for separate specialized stacks, thus reducing overall system complexity
Solution Approach 2:
An intermediary mapping component enables the serialization stack to interpret both formats without requiring format-specific processing logic. This mediator layer abstracts the complexity of format differences, allowing a single serialization stack to maintain simplicity while achieving broad interoperability
3Adaptability or versatility
If data is translated between XML and dynamic language expressions, then adaptability is improved, but processing time and complexity increase
Solution Approach 1:
The system performs preliminary mapping configuration and caching of format conversion rules. By preparing translation parameters and structure mappings in advance, the actual data translation process is accelerated, reducing processing time while maintaining format flexibility
Solution Approach 2:
The mapping mechanism uses efficient parameter transformations that minimize processing overhead. By changing only the necessary structural parameters (ordering, root names) rather than reconstructing entire data structures, the system achieves fast translation between XML and dynamic language expressions
Data Source
AI summary
Converting data to an appropriate format for use with a service. An example method is illustrated where a message including data expressed using dynamic language data expressions is received. The dynamic language data expressions include a tree structure organization for the data. The data expressed using dynamic language data expressions is expressed in an XML data structure. The XML data structure preserves the original tree structure organization for the data.