ChatOps Notification Management with User Priority Scoring
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Solution Overview
Problem
Existing ChatOps systems deliver notifications to a broad audience simultaneously, leading to computational inefficiencies, miscommunication, and user inattention due to irrelevant notifications, without effective notification management.
Innovation Solution
A computer-implemented method trains a model with historical incidents to prioritize and manage notifications based on user-specific group information and system event properties, calculating a notification priority score and delivering notifications to a subset of users at optimized intervals using a batch gradient descent algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If notifications are delivered to a broad audience simultaneously, then information reach is maximized, but computational efficiency deteriorates and user inattention increases
Solution Approach 1:
The system segments the broad audience into distinct user groups based on their roles, responsibilities, and relevance to specific incidents. This segmentation allows notifications to be targeted only to relevant subsets of users rather than broadcasting to everyone, thereby maintaining information reach while improving computational efficiency by reducing the number of notification deliveries.
Solution Approach 2:
The system applies partial action by sending notifications to only the necessary subset of users rather than the complete audience. By calculating notification priority scores and selecting only those users who need to receive the information, the system achieves sufficient information dissemination without the excessive computational cost of universal notification.
2Loss of information
If notifications are delivered to all users, then information completeness is maintained, but user inattention increases due to irrelevant notifications
Solution Approach 1:
The system applies local quality by tailoring notification delivery to the specific needs and characteristics of each user or user group. Rather than uniform notification delivery, the system adjusts notification priority and timing based on individual user relevance scores, ensuring that each user receives notifications appropriate to their role and current context, thereby maintaining information completeness while reducing inattention.
3Measurement precision
If a model is trained with historical incidents and user information, then notification accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary action by training the machine learning model with historical incident data and user information before actual notification delivery. This pre-training phase establishes the notification priority scoring mechanism that will be used during operational phases, allowing the system to achieve high notification accuracy without increasing operational complexity during incident response.
Data Source
AI summary
One or more computer processors training a model with historical incidents, wherein each historical incident is associated with group information and system event properties. The one or more computer processors responsive to a new incident, extract group information from one or more users and extracting system event property information from the new incident. The one or more computer processors calculate a notification priority score for each user utilizing the extracted system event property information and the extracted group information. The one or more computer processors generate a notification based on the new incident. The one or more computer processors transmit the generated notification to the one or more users at a determined interval based on the calculated respective notification priority score for each user and a notification strategy.