Data Flow Architecture Mapping for Self-Updating Context Diagrams
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Solution Overview
Problem
Complex data interactions between computer systems in manufacturing and logistics operations make it difficult to modify software or hardware without causing unexpected system failures, leading to defective products and inefficiencies in training and testing.
Innovation Solution
Automated generation of self-updating system context diagrams using tags associated with processes running on networked computer systems, which collect and store data on source, destination, and data flows to create a data flow model, aiding in training and reducing manual errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual methods are used to document and update system context diagrams, then initial documentation can be created, but the diagrams quickly become outdated and require significant manual effort to maintain
Solution Approach 1:
The system automatically generates and updates system context diagrams by querying tagged data from processes itself, without requiring manual intervention. The diagrams self-update when processes are modified, as the tagging system captures changes automatically.
Solution Approach 2:
The system continuously queries processes for tagged data and uses this feedback to automatically update system context diagrams. When processes change, the feedback loop detects these changes through updated tag data and refreshes the diagrams accordingly.
2Productivity
If complex data interactions between computer systems are modified without proper analysis, then system changes can be implemented quickly, but unexpected system failures and defective products occur
Solution Approach 1:
The system performs preliminary analysis by automatically generating system context diagrams and integration dependency maps before modifications are made. This allows potential issues to be identified in advance, preventing system failures while enabling informed decision-making for quick modifications.
Solution Approach 2:
The patent introduces an intermediary layer of automated documentation and analysis tools that mediate between system modifications and actual changes. This intermediary provides visibility into data interactions and dependencies, allowing safe modifications while maintaining system stability.
3Ease of operation
If extensive manual training is provided to explain system interactions, then new employees can learn system operations, but training timelines are extended and efficiency is reduced
Solution Approach 1:
The system creates visual copies of system context diagrams and integration dependency relationships that serve as self-training materials. New employees can study these automated diagrams to understand system interactions without requiring extensive manual training, significantly reducing training time while maintaining effectiveness.
Data Source
AI summary
Systems, methods, and apparatus related to networked computer systems. In one approach, a central server collects tag data from computer systems on a network. The tag data is associated with processes that execute on the computer systems. The tag data includes source, destination, and data flow information for each process. The collected tag data is stored in a central data repository. The collected tag data is used to generate a data flow model. In one example, the data flow model is a system context diagram that indicates data transfers among the networked computer systems.