Software Lineage Mapping for Real-Time Dependency Visualization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mapping dependencies across complex and dynamic distributed software systems is challenging due to obscured hardware layers, fluid system topologies, and multi-tenant architectures, making comprehensive and accurate component tracking elusive.
Innovation Solution
A system that generates real-time mappings and visualizations of system components and inter-system communications using software applications lineage logs, employing an artificial intelligence model to analyze event data and predict component behavior, allowing for hierarchical workflow mapping and real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive mapping of system components is attempted in distributed architectures, then mapping completeness is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between distributed components. This intermediary collects, normalizes, and stores mapping data from various sources (configuration management databases, service discovery mechanisms, network monitoring tools), thereby simplifying the complexity of tracking distributed components without sacrificing mapping completeness.
Solution Approach 2:
The patent creates a universal mapping system that serves multiple functions: it tracks component relationships, monitors system state, identifies dependencies, and supports troubleshooting. By making the mapping system multi-functional, the patent reduces the need for separate specialized systems, thereby managing complexity while maintaining comprehensive mapping capabilities.
2Speed
If real-time mapping is implemented, then system responsiveness is improved, but computational resources increase
Solution Approach 1:
The patent implements periodic mapping updates triggered by specific events (component registration, deregistration, relationship establishment, configuration changes) rather than continuous real-time mapping. This event-driven periodic approach maintains system responsiveness while significantly reducing computational resource consumption compared to continuous monitoring.
Solution Approach 2:
The patent enables the mapping system to be self-updating by having distributed components automatically register themselves and their relationships with the mapping system. This self-service mechanism eliminates the need for centralized manual configuration, reducing computational overhead while maintaining accurate and up-to-date mappings.
3Measurement precision
If detailed event data analysis is performed, then anomaly detection accuracy is improved, but data processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining normal behavior patterns, baseline statistics, and expected relationships between components. These pre-established profiles are stored in advance, allowing the system to quickly compare current event data against known patterns during runtime, thereby maintaining high anomaly detection accuracy while reducing real-time processing time.
Solution Approach 2:
The patent applies local quality analysis by tailoring the level of detail and analysis depth to the specific context of each event and component. Critical components with high importance receive more detailed analysis, while less critical components are processed with lighter analysis, optimizing the balance between detection accuracy and processing time.
Data Source
AI summary
Systems and methods for real-time mapping and visualization generation of system components. The system may receive a first user request to generate a first visualization of a first configuration of a first subset of components in a first software system. The system may, in response to the first user request, retrieve a first software applications lineage log, wherein the first software applications lineage log comprises a log of event data of current processes being performed in the first software system. The system may generate a first feature input based on the first software applications lineage log. The system may input the first feature input into a first artificial intelligence model to generate a first output.