Social Media Sentiment Alert Aggregation for Software Incidents
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Solution Overview
Problem
Existing software application monitoring systems face inefficiencies in processing a large number of alerts generated by individual software modules, leading to scalability issues and unnecessary computational resources, especially in complex applications.
Innovation Solution
An alert generation system that monitors social media messages related to the software applications, analyzes sentiments, and generates alerts for potential incidents using geo-spatial clustering and machine learning techniques to identify and aggregate relevant information, thereby reducing the need for individual alert processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual software modules generate separate alerts for each issue, then comprehensive monitoring coverage is achieved, but system complexity and computational resource consumption increase significantly
Solution Approach 1:
The patent combines multiple individual module alerts into a single aggregated alert when they represent the same customer-facing issue. The alert aggregation module consolidates alerts from different software modules that relate to the same incident, reducing the number of separate alerts while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent implements a universal alert processing mechanism that handles multiple types of alerts from different software modules through a common aggregation and routing system. This multi-functional approach allows the same infrastructure to process various alert types without requiring separate processing paths for each module.
2Loss of information
If individual software modules generate separate alerts for each issue, then detailed issue tracking is achieved, but computational resources are wasted on processing redundant information
Solution Approach 1:
The alert aggregation module merges redundant alert information by identifying alerts that refer to the same customer-facing issue and consolidating them into a single representative alert, thereby reducing computational processing of duplicate information while preserving complete issue tracking capability.
Solution Approach 2:
The system extracts and removes redundant alert information by identifying and filtering out duplicate or overlapping alerts before they enter the full processing pipeline. This extraction of unnecessary duplicate data reduces computational resource consumption while maintaining accurate issue tracking.
3Loss of information
If traditional monitoring systems process all individual alerts, then complete incident visibility is achieved, but alert processing time and system overhead increase
Solution Approach 1:
The system performs preliminary aggregation and filtering of alerts before they enter the main processing pipeline. By pre-consolidating related alerts and removing obvious duplicates in advance, the system reduces the volume of data requiring full processing, thereby decreasing alert processing time while maintaining complete incident visibility.
Solution Approach 2:
The alert aggregation module combines multiple individual alerts into consolidated incident-level views, allowing the system to process fewer aggregated entities rather than numerous individual alerts. This merging approach maintains complete incident visibility while significantly reducing processing time and system overhead.
Data Source
AI summary
Method and computer readable medium for generating alerts are disclosed. The method includes retrieving a plurality of content items created or updated in a particular time period at a social media platform in relation to a software application of interest, and determining one or more sentiments associated with each of the plurality of content items. The one or more sentiments determined based on textual data of the plurality of content items. The method further includes determining whether a potential incident has occurred in relation to the software application of interest based at least on the one or more sentiments associated with the plurality of content items. In response to determining that a potential incident has occurred in relation to the software application of interest, the method further includes generating an alert message indicating the potential incident and communicating the alert message to a monitoring system.