Automated Entity Standardization for Application Modernization
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Solution Overview
Problem
The modernization of existing applications to use newer computer programming languages, software libraries, and hardware platforms is inefficient due to errors and the need for constituent feedback, especially when dealing with unstructured entities lacking direct textual context or outdated training data.
Innovation Solution
A system and method that automatically standardize raw entities by determining relevant surrounding contexts, matching them with known entities, and identifying entity types without requiring constituent feedback, even in cases where entities are unstructured or lack direct textual context, using a determination component, matching component, and type identification component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive modernization with constituent feedback is used, then entity standardization can be achieved, but the process becomes inefficient and time-consuming due to manual intervention requirements
Solution Approach 1:
The system performs self-service by automatically determining entity types through contextual analysis without requiring external constituent feedback. The determination component autonomously analyzes surrounding contexts, matches entities against knowledge databases, and identifies entity types, eliminating the need for manual intervention and iterative feedback loops while maintaining high accuracy
Solution Approach 2:
The system performs preliminary action by proactively analyzing surrounding contexts and determining entity types before the modernization process begins. The determination component pre-processes entity information by examining contextual data, matching against known entities in knowledge databases, and establishing entity types in advance, thereby eliminating subsequent manual feedback requirements
2Productivity
If active approach with data mining is used, then constituent feedback can be reduced, but outdated training data and lack of surrounding contexts create roadblocks
Solution Approach 1:
The system introduces an intermediary knowledge database that bridges the gap between raw entity data and entity type determination. The matching component uses this intermediary knowledge base to match surrounding contexts with known entity patterns, enabling accurate entity type identification even when training data is outdated or contexts are limited, thus maintaining reliability while improving productivity
3Manufacturing precision
If constituent feedback is requested for error correction, then standardization accuracy can be improved, but the process becomes partially manually driven and timely
Solution Approach 1:
The determination component performs self-service by autonomously analyzing surrounding contexts and determining entity types without requiring external feedback. The system self-corrects potential errors through contextual matching against knowledge databases, eliminating time-consuming feedback iterations while maintaining high standardization accuracy
Solution Approach 2:
The system replaces the mechanical feedback loop with an automated determination mechanism. Instead of relying on manual constituent feedback for error correction, the matching component automatically compares surrounding contexts with known entity patterns in knowledge databases, substituting manual correction processes with automated contextual analysis that maintains accuracy while eliminating time losses
Data Source
AI summary
Systems, computer-implemented methods, and computer program products to facilitate modernization of an application are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a determination component that determines one or more relevant surrounding contexts for a raw entity. The computer executable components also can comprise a matching component that matches the one or more relevant surrounding contexts with one or more known surrounding contexts of one or more known entities. The computer executable components further can comprise a type identification component that identifies an entity type for the raw entity based on the matching of the one or more relevant surrounding contexts with the one or more known surrounding contexts.