Metadata Scanner for Database Redundancy Identification
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Solution Overview
Problem
The increasing redundancy in enterprise databases and application systems due to mergers and the exponential complexity of comparing multiple systems manually lead to inefficiencies, increased costs, and complexity in data access and maintenance.
Innovation Solution
An apparatus and method utilizing a meta data scanner, meta data repository, and enterprise canonical data model to generate CRUD matrices, which identify redundancy by mapping system-specific data elements to an enterprise canonical data model, allowing for automated analysis and consolidation opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison of databases and application systems is performed to identify redundancy, then redundancy can be identified, but the complexity and time required increase exponentially as the number of systems grows
Solution Approach 1:
The patent replaces manual mechanical comparison processes with an automated computer-based system. The system automatically scans metadata from multiple databases and application systems, compares them against an enterprise canonical data model, and identifies redundancy without human intervention. This substitution of manual mechanical processes with automated electronic processing resolves the contradiction by maintaining high measurement precision while eliminating exponential complexity growth.
Solution Approach 2:
The system performs self-service by automatically scanning its own metadata, generating CRUD matrices, and identifying redundancy independently without requiring manual comparison efforts. The automated system serves itself by using its own computational resources to analyze the enterprise data landscape and produce redundancy reports, thereby maintaining accuracy while avoiding the complexity burden of manual methods.
2Adaptability or versatility
If multiple databases and application systems are maintained to support enterprise operations, then functionality and data storage capacity increase, but operational costs and maintenance complexity increase
Solution Approach 1:
The system implements feedback by continuously scanning metadata from databases and application systems, comparing them against the enterprise canonical data model, and generating reports on redundancy and consolidation opportunities. This feedback loop enables ongoing monitoring and identification of optimization opportunities, allowing the system to maintain high functionality while providing actionable insights for reducing maintenance complexity through consolidation.
Solution Approach 2:
The system performs preliminary action by proactively scanning and analyzing metadata before redundancy problems become critical. By continuously monitoring and comparing data structures against the canonical model, the system identifies consolidation opportunities in advance, allowing planned optimizations that maintain functionality while reducing long-term maintenance complexity and costs.
3Productivity
If automated metadata scanning and CRUD matrix generation is implemented, then redundancy identification efficiency improves, but system complexity and initial implementation costs increase
Solution Approach 1:
The patent applies segmentation by dividing the complex redundancy identification task into discrete manageable segments: metadata scanning, CRUD matrix generation, comparison against canonical model, and redundancy reporting. Each segment is handled by a dedicated component or process, making the overall complex system more manageable and implementable while achieving high productivity through automated processing of each segment.
Solution Approach 2:
The system uses an intermediary approach by introducing a canonical data model as a mediator between the diverse databases and application systems and the redundancy analysis. This intermediary standardizes the comparison process, allowing automated scanning and analysis without requiring direct complex interactions between all systems, thereby reducing implementation complexity while maintaining high identification efficiency.
Data Source
AI summary
Apparatuses, computer program products, and methods for identifying redundancy and consolidation opportunities in databases and application systems are disclosed. In one embodiment, the apparatus may include at least one meta data scanner. The apparatus may also include an enterprise meta data source. The apparatus may further include a meta data repository. The meta data repository receives system-specific meta data from the at least one meta data scanner. The meta data repository may also receive enterprise canonical data model meta data from the enterprise meta data source. The meta data repository may be configured to generate at least one individual system CRUD matrix that may then used to produce an enterprise canonical model CRUD matrix. The enterprise canonical model CRUD matrix may be analyzed by a data mining clustering algorithm. The clustering algorithm may group together modules and database elements that may be redundant and may indicate opportunities for consolidation.