Event Clustering System with Floating Point Unit for Infrastructure Alerts
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Solution Overview
Problem
Current systems for managing and organizing vast amounts of electronic messages and events from infrastructure, such as emails and news groups, lack effective automated techniques for indexing and clustering, leading to difficulties in retrieval and productivity due to the high volume of spam and irrelevant information.
Innovation Solution
An event clustering system that includes a feedback signalizer engine with a floating point unit, utilizing machine learning approaches to identify common characteristics among events and create clusters related to failures or errors in managed infrastructure, enabling supervised learning to replicate situations and improve precision over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual organization of messages into folders is used, then information can be sorted by topic, but the process becomes impractical when handling hundreds of messages per day
Solution Approach 1:
The system automatically analyzes incoming messages and assigns them to appropriate folders without user intervention. The automated message organizer examines message content, metadata, and user preferences to perform classification tasks that would otherwise require manual user effort, thereby eliminating the time burden while maintaining organizational quality
Solution Approach 2:
The manual mechanical process of dragging and dropping messages into folders is replaced by an automated software system that uses algorithms to perform classification. This substitution transforms a labor-intensive manual operation into an automated computational process, resolving the contradiction between ease of operation and time consumption
2Productivity
If automated clustering techniques are implemented, then message organization efficiency improves, but the system complexity increases due to the need for advanced algorithms and processing power
Solution Approach 1:
The automated message organizer is implemented as a modular system with distinct components: message analysis module, classification module, and folder management module. This segmentation allows each component to be independently optimized and maintained, reducing overall system complexity while preserving high productivity through specialized processing in each module
Solution Approach 2:
The system introduces an intermediary automated organizer that acts as a buffer between incoming messages and the user's folder structure. This intermediary layer handles the complex classification logic, shielding the user from system complexity while maintaining high processing efficiency through automated decision-making
3Ease of operation
If traditional folder systems are used, then messages can be stored in organized categories, but retrieval becomes difficult when messages are divided across multiple folders
Solution Approach 1:
The automated message organizer provides multiple functions: it not only sorts messages into folders but also creates cross-folder connections, generates indexes, and enables searching across the entire message set regardless of folder location. This multi-functionality ensures that messages remain accessible through multiple pathways, resolving the retrieval difficulty caused by strict folder divisions
Solution Approach 2:
The system continuously monitors user retrieval patterns and feedback to improve its organization and indexing strategies. By learning from how users actually search and retrieve messages, the system adapts its folder structure and cross-referencing to optimize both organization and retrieval ease, preventing information loss across folder boundaries
Data Source
AI summary
An event clustering system and associated methods include a feedback signalizer functor that responds to user interactions with already formed situations. The system and method then learns how to replicate the same situation when new alerts reoccur, or, creates similar situations. The feedback signalizer functor is a supervised machine learning approach to train a signalizer functor to reproduce a situation at varying degrees of precision.