Cloud Integration Mapping Recommendation Engine
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Solution Overview
Problem
The complexity of enterprise application integration, particularly in shifting from on-premises to hybrid Software-as-a-Service (SaaS) environments, leads to challenges in simplifying data mapping between different applications, making it time-consuming and error-prone.
Innovation Solution
A system and method for providing recommended mappings in a cloud-based integration service, where a database stores mapping records with ratings generated by a ranking engine, and a recommendation engine retrieves and displays these records for graphical display in a mapper, allowing developers to accept or reject them, updating the database with actual mappings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data mapping is performed between source and target applications, then mapping accuracy can be maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service through automated mapping recommendation where the recommendation engine automatically generates mapping suggestions between source and target data objects without requiring manual configuration. The engine analyzes data schemas, identifies matching fields, and provides recommended mappings that developers can review and accept, significantly reducing manual effort while maintaining accuracy through automated intelligent recommendations.
Solution Approach 2:
The patent replaces the mechanical manual mapping process with an automated recommendation engine that uses algorithmic analysis to generate mapping suggestions. Instead of manually comparing and matching data fields, the system employs automated data object analysis, schema comparison, and intelligent recommendation algorithms to substitute the manual mechanical process with an automated computational system.
2Productivity
If automated mapping recommendations are provided, then time consumption is reduced, but the complexity of the system increases
Solution Approach 1:
The recommendation engine serves as an intermediary component between the data source and target applications. It receives data object definitions, analyzes schemas, generates mapping recommendations, and presents them to developers for review. This intermediary layer manages the complexity internally while presenting a simplified interface to users, allowing high productivity without exposing the full system complexity to end users.
Solution Approach 2:
The system segments the integration process into distinct modular components: data object definition, schema analysis, recommendation generation, and mapping acceptance. Each component handles a specific aspect of the mapping process independently, allowing the complex automated recommendation functionality to be built from manageable segments that can be developed, maintained, and scaled separately.
3Stability of the object's composition
If mapping records are stored and reused, then consistency across integrations is improved, but the database management complexity increases
Solution Approach 1:
The system creates and stores mapping records as reusable templates in a database. Once a mapping is established and validated, it is copied and stored for future use in similar integration scenarios. The recommendation engine queries this database to retrieve previously successful mappings, ensuring consistency across different integrations while managing complexity through standardized reusable mapping templates rather than custom configurations for each integration.
Data Source
AI summary
In accordance with an embodiment, described herein is a system and method for providing recommended mappings to a mapper for use in designing an integration flow in a design time of a cloud-based integration service. A database can store mapping records from a plurality of sources, and mapping records inferred from the extracted mapping records. Each mapping record in the database can be associated with a rating generated by a ranking engine. A recommendation engine can be invoked to retrieve one or more mapping records from the database and an auto suggestion engine, for graphical display in the mapper. The integration flow can be published, and mapping information therein can be parsed by the recommendation engine into one or more mapping records, which are persisted into the database to update the mapping records in the database.