Alert Cluster List Interface for Bulk Alert Management
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Solution Overview
Problem
Existing alert management systems generate excessive volumes of alerts, leading to alert overload and fatigue, which can result in errors and potential service outages, particularly in service-oriented platforms with numerous interdependent services and microservices.
Innovation Solution
A system that groups alerts into clusters using an alert clustering model, providing a consolidated alert cluster list interface with engagement components for bulk actions, and includes a feedback loop to train and optimize alert classification, reducing cognitive load and improving alert management efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alerts are managed individually in traditional alert management systems, then each alert can be handled with detailed attention, but alert overload and fatigue occur due to excessive volumes of alerts
Solution Approach 1:
The patent segments the large volume of alerts into smaller, manageable clusters based on similarity metrics. Each cluster groups related alerts together, allowing operators to handle multiple alerts as a single unit. This segmentation reduces the cognitive load and operational complexity while maintaining reliable alert management through structured organization.
Solution Approach 2:
The patent merges similar alerts into consolidated cluster representations. By combining multiple individual alerts into a single cluster entity with aggregated information, the system reduces the number of discrete items operators must process. This merging approach maintains comprehensive monitoring coverage while significantly improving ease of operation.
2Productivity
If bulk actions are implemented on alert clusters, then alert handling efficiency improves through consolidated operations, but precision in individual alert management may be reduced
Solution Approach 1:
The patent applies local quality by allowing different levels of interaction with clustered alerts. Operators can perform bulk actions on entire clusters for routine operations, while also maintaining the ability to drill down into individual alert details when precision is required. This hierarchical approach enables both high-level productivity through bulk operations and fine-grained precision when needed.
Solution Approach 2:
The patent implements dynamic alert management where the level of aggregation can be adjusted based on operational needs. The system allows operators to dynamically switch between viewing and managing alerts as individual items or as clustered groups. This dynamic flexibility enables optimization between productivity (bulk actions) and precision (individual management) based on the specific situation.
3Ease of operation
If alert clustering is implemented to reduce alert volumes, then cognitive load decreases and alert fatigue is reduced, but system complexity increases due to clustering algorithms
Solution Approach 1:
The patent implements self-service alert clustering where the system automatically performs clustering operations without requiring manual configuration or intervention. The clustering algorithms autonomously analyze alert patterns, group similar alerts, and present organized clusters to operators. This self-service approach masks the underlying system complexity while delivering simplified operational interfaces.
Solution Approach 2:
The patent introduces an intermediary clustering layer between the raw alert stream and the operator interface. This intermediary component handles the complex processing of alert analysis and grouping, presenting a simplified view to operators. The intermediary absorbs the system complexity internally while providing ease of operation externally through intuitive cluster-based interfaces.
4Measurement precision
If traditional alert management processes are used, then individual alert analysis is thorough, but time consumption increases and response efficiency decreases
Solution Approach 1:
The patent applies preliminary action by pre-processing and pre-grouping alerts into clusters before they reach the operator. The system performs preliminary analysis to identify patterns and relationships among alerts, organizing them into logical groups in advance. This preliminary organization reduces the time required for operators to analyze individual alerts while maintaining thoroughness through structured presentation of relevant information.
Solution Approach 2:
The patent enables continuous monitoring and analysis of alerts through the clustering mechanism. Rather than processing alerts in discrete, time-consuming individual steps, the system continuously aggregates and updates cluster information as new alerts arrive. This continuous useful action maintains thorough alert analysis while significantly reducing time loss through automated, ongoing processing.
Data Source
AI summary
Various embodiments disclosed herein are directed to a system, method, apparatus, and/or a computer program product that are configured to create an alert cluster list interface for viewing alert clusters (rather than individual alerts) in an alert management system. The alert clusters presented via the alert cluster list interface are programmatically classified as similar or related using an alert clustering model as discussed herein. By providing the alert cluster list interface, various embodiments provide an automated and consolidated view of the alert landscape presented at any given time within a software application framework.