Graphical Mapping Recommendations in Cloud Integration
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Solution Overview
Problem
The integration of on-premises and cloud-based applications poses challenges in simplifying enterprise application integration, particularly in visualizing and efficiently mapping data objects of different formats, which is time-consuming due to hierarchical structures and multiple potential mappings.
Innovation Solution
A system and method for graphically displaying recommended mappings between source and target data objects in a cloud-based integration service design time, utilizing a recommendation engine that provides filtered and rated mappings, allowing users to toggle between actual and recommended mappings, and associate reliability indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If developers manually map data objects between source and target applications, then mapping precision can be controlled, but the time required for integration increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating recommended mappings between data objects before the developer finalizes the integration. The recommendation engine analyzes source and target data structures in advance and proposes optimal mappings, reducing the time developers need to spend on manual mapping while maintaining precision through developer review and adjustment capabilities.
2Quantity of substance
If the system displays all possible mappings between data objects, then mapping completeness is achieved, but the complexity of the interface increases
Solution Approach 1:
The system extracts and presents only the most relevant mappings through the recommendation engine, which filters out less suitable mapping options. This extraction approach maintains mapping completeness by considering all possible mappings while reducing interface complexity by displaying only the top recommended mappings that are most likely to be appropriate for the given integration scenario.
Solution Approach 2:
The interface applies local quality by providing different levels of mapping detail based on the specific data objects and their relationships. The recommendation engine analyzes the local context of each data object pairing and presents mappings with appropriate levels of detail and relevance, avoiding a uniform display of all possible mappings and thereby reducing overall interface complexity.
3Manufacturing precision
If the system provides detailed mapping options for hierarchical data structures, then mapping accuracy improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system implements feedback mechanisms where the recommendation engine continuously refines mapping suggestions based on the hierarchical structure analysis. The interface provides feedback to developers about the quality and suitability of recommended mappings, making it easier to detect and measure mapping accuracy while maintaining detailed options for hierarchical data structures.
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for graphically displaying recommended mappings between a source data object and a target data object in a design time of a cloud-based integration service. The system can include a recommendation engine that provides recommended mappings between the source and target data objects, so that the recommended mappings can be graphically displayed in a mapping interface. The recommended mappings can be filtered based one or more filtering criteria. Each recommended mapping can be displayed differently from an actual mapping, and can be associated with a reliability/quality indicator. A particular recommended mapping can be accepted to become an actual mapping, or to be rejected. The system allows a user to toggle between actual mappings and recommended mappings between the source and target data objects.