Mashup Infrastructure Learning Mechanism for Port Connection
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Solution Overview
Problem
In mashup environments, users face difficulties in connecting components from different technologies due to mismatched interfaces, requiring manual bridging and technical expertise, which hinders efficient data exchange and customization.
Innovation Solution
A learning mechanism that identifies and suggests potential connections between mashup application ports based on tagged parameters and historical connections, using a mashup model to abstractly define entities and connections, and evolve over time with user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual bridging is used to connect mashup components with different interfaces, then connections can be established, but technical expertise is required and the process becomes complex and time-consuming
Solution Approach 1:
The system performs automatic interface matching and connection establishment without requiring manual intervention. The learning mechanism autonomously analyzes component interfaces, identifies compatible connection points, and creates connections based on historical patterns and tagged parameters, eliminating the need for users to manually bridge components.
Solution Approach 2:
The patent replaces the manual mechanical process of connecting components with an automated intelligent system. Instead of users manually matching interfaces and creating connections, a learning mechanism with artificial intelligence automatically performs interface analysis, compatibility assessment, and connection establishment based on historical data and patterns.
2Productivity
If automatic connection suggestions are implemented, then ease of operation improves, but the system requires learning mechanisms and historical data processing
Solution Approach 1:
The system performs preliminary analysis of component interfaces and stores connection patterns in advance. By pre-processing interface information and maintaining a repository of historical connections with tagged parameters, the learning mechanism is prepared to quickly suggest and establish connections without requiring complex real-time computation during the actual connection process.
Solution Approach 2:
The learning mechanism incorporates feedback from historical connection data and user interactions to continuously improve its suggestion accuracy. By analyzing past successful connections, tagged parameters, and interface compatibility patterns, the system refines its learning model to provide increasingly accurate connection suggestions, reducing the need for complex manual intervention.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for providing a mashup infrastructure with a learning mechanism. One process includes operations for receiving a request for connecting a first port of an application with a different port and identifying tagged parameters associated with the first port. A set of potential ports for connecting with the first port based at least in part on the tagged parameters is dynamically determined. A suggestion of potential ports for connecting with the first port, including at least a subset of potential ports selected from the set of potential ports, is presented.