Event Clustering System Using Numerical Feature Vectors
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Solution Overview
Problem
Current systems for managing and organizing vast amounts of web-based information, such as emails and messages, lack effective automated techniques for indexing and retrieval, leading to difficulties in finding relevant information due to the impracticality of manual folder organization and the inefficiency of existing spam detection methods.
Innovation Solution
An event clustering system that converts non-numerical parameters into numerical representations using an extraction engine with NMF, k-means clustering, and topology proximity engines to create feature vectors for events, facilitating the grouping of similar events and improving information retrieval and spam detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual folder organization is used to manage web-based information, then information can be stored in topic-based folders, but it becomes impractical to handle and organize large volumes of information efficiently
Solution Approach 1:
The system automatically analyzes and clusters events without human intervention by converting event parameters to numerical representations and applying clustering algorithms, enabling the system to self-organize large volumes of information that would be impractical to manage manually
Solution Approach 2:
The patent replaces manual mechanical folder organization with an automated computational system that uses parameter conversion and clustering algorithms to organize information, substituting human effort with machine-based automatic classification
2Productivity
If automated indexing techniques are implemented to improve information retrieval, then retrieval efficiency increases, but the system complexity increases due to the need for sophisticated detection and measurement capabilities
Solution Approach 1:
The system changes the parameter representation of events by converting non-numerical event parameters into numerical representations, enabling the use of mathematical clustering algorithms while maintaining manageable system complexity through standardized parameter transformation
Solution Approach 2:
The patent introduces an intermediary parameter conversion process that bridges raw event data and clustering algorithms, transforming diverse event parameters into a unified numerical format that simplifies the overall system architecture while enabling automated retrieval
3Object-affected harmful factors
If existing spam detection methods are used, then some spam filtering is achieved, but the methods are inefficient and cannot handle the vast amount of information effectively
Solution Approach 1:
The system segments the problem of spam detection by clustering events based on their parameter similarities, separating spam events from legitimate events through automatic grouping, which enables efficient handling of large data volumes through distributed processing of event clusters
Data Source
AI summary
An event clustering system includes an extraction engine in communication with a managed infrastructure. A sigalizer engine that includes one or more of an NMF engine, a k-means clustering engine and a topology proximity engine. The sigalizer engine determines one or more common characteristics or features from events that includes one or more event parameters. The sigalizer engine uses the common features of events to produce clusters of events relating to the failure or errors in the managed infrastructure. Membership in a cluster indicates a common factor of the events that is a failure or an actionable problem in the physical hardware managed infrastructure directed to supporting the flow and processing of information. Each of an event parameter is converted into a numerical representation.