Data Transformation Component for Legacy Architecture Modernization
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Solution Overview
Problem
Transforming operational data from a legacy architecture to an updated architecture is challenging, as existing methods fail to effectively decompose and utilize operational data, leading to its unavailability and unattainability, which hinders issue-spotting and problem-solving in new or updated systems.
Innovation Solution
A system and method that employs machine learning techniques to transform original operational data into updated operational data, using a processor to extract, resolve, disentangle, and cluster data elements, enabling their use in new architectures, and employing AI to match data elements with aspects of the updated architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operational data is not transformed during architecture modernization, then the architecture transformation is simpler and faster, but the operational data becomes unavailable and unattainable for the new architecture
Solution Approach 1:
The patent introduces an intermediary data transformation system that acts as a bridge between legacy operational data and the modernized architecture. This intermediary component automatically maps, transforms, and adapts operational data from the legacy system to the new architecture, making the data available without requiring manual intervention or complex manual transformation processes.
Solution Approach 2:
The patent implements preliminary action by performing data transformation automatically during the architecture modernization process itself. Rather than waiting for post-modernization data migration efforts, the transformation is embedded in the modernization workflow, preparing the operational data in advance for its new architectural context.
2Productivity
If operational data from legacy architecture is made available for the updated architecture, then problem-solving and issue-spotting improve, but the complexity of data transformation increases
Solution Approach 1:
The patent implements self-service by enabling the operational data to automatically adapt to the new architecture through automated transformation processes. The system performs self-mapping and self-transformation of data structures, eliminating the need for manual data preparation and reducing the complexity burden on operators while maintaining high productivity in problem-solving activities.
3Device complexity
If manual methods are used to decompose operational data, then transformation complexity is reduced, but the data becomes unattainable and unavailable for automated use
Solution Approach 1:
The patent replaces manual mechanical decomposition methods with automated computational transformation systems. Machine learning models and automated mapping algorithms substitute for manual data decomposition, enabling the system to automatically process, transform, and make operational data accessible in the new architecture without losing information or requiring manual intervention.
Data Source
AI summary
One or more systems, computer-implemented methods and/or computer program products to facilitate a process to transform original operational data into updated operational data. 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 transformation component that can transform original operational data of a first architecture into updated operational data employable at a second architectures, wherein the second architectures is an updated architectures relative to the first architecture. In one or more embodiments, the transformation component further can employ machine learning to match one or more data elements of the original operational data to one or more aspects of the second architecture.