Automatic Workflow Model Generation from Network Event Logs
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Solution Overview
Problem
Conventional Knowledge Management systems are inadequate in operational contexts like telecommunications, as they fail to manage and codify feedback knowledge, assume repetitive tasks, and require extra effort from staff to explicit knowledge, and are unable to automatically update operational knowledge, especially when tasks involve manual activities.
Innovation Solution
A method and system for automatically generating workflow models from network equipment logs using approximate regular expression matching, which detects insertion errors and integrates with resource proxy agents to record and analyze commands and events, enabling the creation of workflow models without additional staff effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional Knowledge Management systems are used to manage operational knowledge, then knowledge can be codified and distributed, but the systems require extra effort from staff to explicit knowledge and cannot automatically update operational knowledge
Solution Approach 1:
The system enables automatic generation of workflow models by analyzing event logs from network equipments without requiring manual intervention from operators. The resource proxy agents automatically record commands and events, and the workflow model generator automatically creates updated workflow models, making the knowledge management system self-updating and eliminating the need for staff to explicitly encode knowledge.
Solution Approach 2:
The patent replaces manual knowledge encoding processes with automated computational methods. Regular expression matching algorithms automatically analyze event logs to extract workflow patterns, substituting the mechanical process of manual knowledge documentation with automated text and pattern analysis systems.
2Adaptability or versatility
If conventional Knowledge Management systems are used, then knowledge can be structured and distributed, but they assume repetitive tasks and cannot handle manual activities effectively
Solution Approach 1:
The system dynamically adapts to different types of tasks including manual activities by continuously analyzing event logs and automatically updating workflow models. Rather than assuming fixed task types, the system evolves its understanding of operational processes based on actual observed events, making it versatile enough to handle both repetitive and manual tasks equally effectively.
3Extent of automation
If automatic workflow derivation from event logs is implemented, then workflow models can be generated without manual effort, but the system cannot detect insertion errors in regular expression matching
Solution Approach 1:
The system incorporates feedback mechanisms where the results of regular expression matching are continuously evaluated and refined. When insertion errors are detected in workflow model generation, the system uses this feedback to improve future matching accuracy, creating a self-correcting automated process that maintains both high automation and precision.
Data Source
AI summary
A system for automatic generation of workflow models related to interventions performed on equipment included in a communication network having associated resource proxy agents each providing a representation of the status of corresponding network equipment according to a given data model. The system includes a set of recorder agents, and the resource proxy agents are configured to send to the recorder agents information signals representative of events in the status of the corresponding network equipment triggered by manual activities or commands, such as, commands input by operators and performed on the network equipment. The system is configured to analyze the information signals sent to the respective recorder agents to produce therefrom workflow models of the manual activities or commands performed on the network equipment.