Application Suggestion System for Portal Integration
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Solution Overview
Problem
Integrating new or enhanced applications into existing portal environments is challenging due to the need for manual connectivity and user interface modifications, leading to increased Total Cost of Development and Ownership, and often results in new applications going unnoticed by users.
Innovation Solution
A method and apparatus that automatically suggest relevant applications to users by matching metadata, converting data formats, and prioritizing suggestions, allowing for easy invocation with relevant parameters, thereby reducing manual work and enhancing user awareness of available applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If new applications are manually integrated into the portal, then connectivity and functionality are established, but development cost and integration effort increase significantly
Solution Approach 1:
The system performs self-service by automatically detecting available applications, analyzing their metadata, and generating integration configurations without manual intervention. The portal system itself identifies which applications should be connected based on metadata matching, eliminating the need for developers to manually establish connectivity.
Solution Approach 2:
Metadata serves as an intermediary layer between applications and the portal integration system. By analyzing metadata from both the portal and candidate applications, the system mediates the integration process, automatically determining compatibility and generating appropriate connection configurations without direct manual pairing.
2Ease of operation
If manual user interface modifications are made for each application, then application accessibility is improved, but total cost of ownership increases
Solution Approach 1:
The system automatically generates user interface configurations by analyzing application metadata and portal context. Instead of requiring developers to manually create interface elements for each application, the system self-generates appropriate interface configurations and automatically integrates them into the portal.
Solution Approach 2:
The system performs preliminary analysis of application metadata before integration to pre-determine the appropriate user interface configurations. By analyzing metadata in advance, the system prepares integration configurations beforehand, eliminating the need for post-integration interface modifications.
3Loss of information
If all available applications are displayed to users, then application visibility is maximized, but display area constraints are exceeded
Solution Approach 1:
The system applies local quality by providing personalized application suggestions to each user based on their specific context, role, and usage patterns. Instead of displaying all applications uniformly to all users, the system tailors the displayed applications to match individual user needs and portal context.
Solution Approach 2:
The system dynamically changes display parameters by adjusting which applications are suggested based on multiple factors including user role, portal context, application metadata, and usage patterns. This parameter-based filtering optimizes display content to show only relevant applications within available space.
4Adaptability or versatility
If data format conversions are performed manually for application integration, then data compatibility is achieved, but integration time and resources increase
Solution Approach 1:
The system automatically performs data format conversion by analyzing metadata from source and target applications. It self-determines the required transformations and executes the conversions without manual intervention, matching data formats based on metadata analysis between applications.
Solution Approach 2:
The system performs preliminary data format analysis by examining metadata before integration to pre-determine required conversions. By analyzing data formats in advance through metadata, the system prepares appropriate conversion routines beforehand, eliminating time-consuming manual format matching during integration.
Data Source
AI summary
A method and apparatus for automatically suggesting further applications to a user using an executed application in a computerized environment, comprising receiving metadata provided by the executed application; searching an index for suggested applications which receive as input the data provided by the executed application; assigning a priority for each of the suggested applications; sorting the suggested applications according to the priority; and displaying to the user a list comprising the applications that received the highest priorities.