Real-Time Software System Mapping via AI Workflow Analysis
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Solution Overview
Problem
Mapping system components in distributed architectures is technically challenging due to dynamic, distributed, and complex software systems, which obscure underlying hardware and have fluid system topologies.
Innovation Solution
The system generates hierarchical workflow mappings using event data from software applications lineage logs and component repositories, employing an artificial intelligence model to create real-time mappings and visualizations of system components and inter-system communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to track system components, then individual component monitoring is achievable, but understanding connections and dependencies between components becomes impossible
Solution Approach 1:
The system segments the monitoring task into two complementary parts: individual component monitoring and relationship mapping. Event data is collected at the component level while simultaneously tracing dependency relationships between components, allowing both precise component-level monitoring and comprehensive system-level dependency understanding to coexist without conflict.
2Loss of information
If comprehensive mapping of all system components is performed, then complete system visibility is achieved, but the complexity of processing and managing the mapping data increases significantly
Solution Approach 1:
The system extracts only the essential dependency relationships from the complete system mapping data. By identifying and extracting critical upstream and downstream dependencies rather than processing all possible component relationships, the system achieves comprehensive visibility while managing data processing complexity through selective extraction of meaningful connections.
Solution Approach 2:
The system applies different levels of mapping detail to different parts of the system based on their importance and complexity. Critical components with significant dependencies receive more detailed mapping and analysis, while less critical components use simplified representations, allowing comprehensive coverage while reducing overall processing complexity through localized detail optimization.
3Measurement precision
If real-time mapping is implemented to capture dynamic system changes, then current system state accuracy is improved, but the computational resources and processing time required increase
Solution Approach 1:
The system implements periodic updates of the component dependency map at strategically determined intervals rather than continuous real-time updates. Event data is collected continuously but the comprehensive mapping is refreshed periodically when significant system changes occur, maintaining accurate system state information while reducing computational resource consumption by avoiding unnecessary continuous processing.
Data Source
AI summary
Systems and methods for real-time mapping and visualization generation of system components and inter system communications as well as for the generation of real-time recommendations for architectural recommendations. The systems and methods generate hierarchical workflow mappings of a computational network using event data from software applications lineage logs as well as component and artifact repositories. For example, while event data in software applications lineage logs is conventionally limited to identifying that a given process occurred, the systems and methods use the plurality of events detailed in the software applications lineage logs to create, using an artificial intelligence model, a network mapping of how system components are arranged and interact.