Enterprise Portal Meta-Model Inconsistency Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprise portal system administrators face difficulties in managing content entities due to complexity and inconsistencies in content meta-models, leading to high total cost of ownership.
Innovation Solution
A computer-implemented method for updating enterprise portal content meta-models by analyzing content repositories for inconsistencies, receiving modification suggestions, and applying these suggestions to rectify issues, thereby maintaining a consistent state and reducing administrative costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If administrators manually manage and update content meta-models, then they can control changes, but the complexity and inconsistency of the meta-model increases over time
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing content entities against the meta-model to detect inconsistencies. It then self-corrects by generating and applying modifications to the meta-model without requiring manual administrator intervention for each inconsistency, thereby maintaining reliability while reducing administrative complexity
Solution Approach 2:
The system implements a feedback loop where the content analyzer continuously monitors the meta-model and content entities, generates reports on inconsistencies, and enables automatic application of corrections. This closed-loop feedback mechanism ensures the meta-model remains consistent while automating the administration process
2Adaptability or versatility
If administrators frequently update the meta-model to reflect changes, then the system adapts to new requirements, but inconsistencies and errors increase
Solution Approach 1:
The content analyzer performs preliminary analysis of content entities against the current meta-model to identify potential inconsistencies before they cause problems. The system proactively generates modification suggestions and can automatically apply them, preventing inconsistencies from accumulating while the system adapts to new requirements
Solution Approach 2:
The system automatically manages its own evolution by analyzing content changes, generating appropriate meta-model modifications, and applying them without human intervention. This self-service approach ensures accurate adaptations while minimizing errors that would result from manual updates
3Manufacturing precision
If the meta-model is highly detailed and comprehensive, then it provides precise control over content entities, but the difficulty of administration increases
Solution Approach 1:
The content analyzer automatically performs the complex task of comparing content entities against the detailed meta-model and generating precise modifications. This automation handles the precision requirements while eliminating the administrative burden, making the system both precise and easy to operate
Solution Approach 2:
The system replaces manual mechanical administration processes with automated computer-based analysis and modification generation. The content analyzer uses computational algorithms to handle the detailed comparisons and generate precise meta-model updates, eliminating the need for manual administration while maintaining high precision
Data Source
AI summary
The disclosure generally describes computer-implemented methods, software, and systems for updating enterprise portal content meta-models. One computer-implemented method includes receiving a content analysis request associated with a content repository, analyzing, using at least one computer, the content repository content objects for inconsistencies with a meta-model, receiving meta-model modification suggestion data, modifying the meta-model using the meta-model modification suggestion data, and receiving modification status data.