Intelligent Message Mapping with Real-Time Payload Visualization
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Solution Overview
Problem
Cloud integration processes are complex and time-consuming due to the high learning curve of conventional message mapping tools, which require extensive knowledge and result in inefficient development and error-prone integration scenarios, especially when dealing with large datasets and diverse payload structures.
Innovation Solution
A mapping module that provides real-time visualization of both input and expected output payloads within the module, allowing developers to design message maps without leaving the tool, and uses machine learning to suggest transformation functions based on payload analysis, thereby reducing the need for external documentation and minimizing re-work.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional message mapping tools are used, then integration functionality can be achieved, but the learning curve is high and development efficiency is low
Solution Approach 1:
The system performs automatic payload analysis and transformation function suggestion without requiring developers to manually consult documentation or guides. The analysis module automatically examines payload structures and proposes appropriate transformation functions, allowing the system to serve itself rather than requiring extensive developer expertise.
Solution Approach 2:
The patent replaces manual mechanical processes (developers manually reading documentation, manually configuring mappings) with automated computational processes (machine learning models analyzing payloads and suggesting transformations). This substitution reduces the complexity burden on developers while maintaining integration functionality.
2Reliability
If complete payload simulation is performed, then integration accuracy can be verified, but development time is significantly increased
Solution Approach 1:
The system performs preliminary payload analysis during the design phase rather than waiting for complete implementation. By analyzing payload structures early and suggesting transformation functions beforehand, the system enables developers to verify integration accuracy without completing the entire payload simulation process.
Solution Approach 2:
Instead of requiring complete payload simulation for verification, the system performs partial analysis on payload structures and applies transformation functions selectively. This partial action approach provides sufficient verification for development purposes without the time cost of complete simulation.
3Reliability
If tracing is performed on every value conversion, then errors can be detected, but the amount of information generated is overwhelming
Solution Approach 1:
The system extracts only the essential transformation functions and critical mapping relationships from the complete payload processing. Rather than tracing every single value conversion, it identifies and tracks only the key transformation steps that are most relevant for error detection and developer understanding.
4Loss of information
If re-execution of simulation is required to get information at different steps, then complete context is available, but developers lose context from previous steps
Solution Approach 1:
The system maintains continuous tracking of transformation functions and mapping context throughout the development process. Rather than requiring re-execution to retrieve context, the system continuously preserves and makes accessible the state of transformations at all steps, allowing developers to review context without losing information from previous steps.
Data Source
AI summary
A method and system include a source endpoint and a target endpoint; a mapping module; and a mapping processor in communication with the mapping module and operative to execute processor-executable process steps. An input data is received including one or more payload data values and a payload schema data. An expected target data is received. The expected target data includes one or more expected target payload values and one or more expected target schema. The expected target data are in a form receivable by the target endpoint. One or more fields of payload data values are identified in the input data for transformation. At least one function is identified to transform the input data into the expected target data. The identified at least one function and input data form an expression step. The expression step is received at a first interface. The expression step is executed in the first interface to generate a generated output in the first interface. It is determined whether the generated output matches the expected target data. An indication of the match is provided to at least one of a user interface and another system. Numerous other aspects are provided.