Social Early Warning System for Computer Problem Detection
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Solution Overview
Problem
Existing systems fail to efficiently detect computer system problems, such as hardware failures, software bugs, network issues, and human errors, due to their limited ability to monitor and analyze social media posts for indicative keywords, leading to delayed or missed alerts.
Innovation Solution
A social early warning system that monitors social network sites for predefined keywords, tags relevant posts, and raises alerts when certain thresholds are met, allowing for quick detection of system issues like outages, security breaches, or slow performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If social media posts are monitored using traditional methods, then system problems can be detected, but the detection speed and efficiency are insufficient leading to delayed alerts
Solution Approach 1:
The system pre-identifies and tags posts containing keywords indicative of system problems before actual incidents occur. By preparing tagged posts in advance and establishing threshold criteria beforehand, the system eliminates processing delays when problems arise, enabling immediate detection and alerting when thresholds are met.
Solution Approach 2:
The patent replaces traditional manual or rule-based monitoring systems with a machine learning-based automated system. The ML model automatically analyzes social media posts, identifies relevant keywords, and determines system problems without human intervention, significantly improving detection speed and reducing time delays compared to conventional methods.
2Reliability
If comprehensive monitoring of all social media posts is performed, then all system problems can be detected, but the computational resources and complexity increase significantly
Solution Approach 1:
The system extracts only the most relevant information from social media posts by identifying and focusing on specific keywords indicative of system problems. Rather than analyzing entire posts or all social media data, the system pulls out key terms and phrases that signal potential issues, reducing computational complexity while maintaining detection reliability.
Solution Approach 2:
The patent segments the monitoring task into distinct components: keyword identification, post tagging, threshold comparison, and alert generation. By dividing the comprehensive monitoring process into these manageable segments, the system reduces overall complexity while ensuring thorough problem detection through systematic analysis of each component.
3Measurement precision
If manual analysis of social media posts is used, then detailed investigation can be performed, but the process is time-consuming and costly
Solution Approach 1:
The patent replaces manual human analysis with an automated machine learning system that performs post analysis, keyword identification, and problem determination. This substitution maintains high measurement precision through sophisticated algorithms while eliminating the time-consuming nature of manual review and reducing operational costs.
Solution Approach 2:
The system performs self-service analysis by automatically monitoring social media posts, identifying relevant keywords, tagging posts, and determining system problems without requiring continuous human intervention. The automated ML model serves itself by continuously learning from data and making independent determinations, providing precise analysis at scale without manual effort.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, to detect system problems using social media. One of the method includes monitoring posts on a plurality of social network sites. The method includes identifying posts that include at least one of a plurality of keywords, at least some of the keywords indicative of system problems. The method includes tagging the identified posts. The method includes determining, based on the tagged posts, that a system problem is occurring. The method also includes raising an alert regarding the system problem.