Event Clustering System for Infrastructure Failure Analysis
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Solution Overview
Problem
Current systems for managing and organizing vast amounts of web-based communication, such as email and news groups, lack effective automated techniques for indexing and retrieval, leading to difficulties in finding relevant information due to the manual and hierarchical organization methods, which are impractical for the high volume of data generated daily.
Innovation Solution
An event clustering system that utilizes an extraction engine to convert messages from managed infrastructure into clusters related to failures or errors, employing Non-negative Matrix Factorization (NMF), k-means clustering, and topology proximity engines to determine common characteristics and produce actionable clusters, facilitating the identification of common factors and actionable problems in the infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual hierarchical organization methods are used to manage web-based communication, then information can be stored in folders, but it becomes impractical to handle the massive amounts of data generated daily and difficult to locate relevant information
Solution Approach 1:
The system automatically indexes and organizes web-based communication data without requiring manual user intervention. The extraction engine autonomously processes messages, identifies topics, and creates hierarchical structures, allowing the system to serve itself rather than requiring users to manually categorize vast amounts of data
Solution Approach 2:
The patent replaces manual mechanical organization methods with automated computational processes. The extraction engine uses text analysis and pattern recognition algorithms to automatically categorize and index data, substituting human manual sorting with machine-based automated classification systems
2Loss of time
If automated indexing techniques are implemented to handle large volumes of data, then information retrieval becomes more efficient, but the system complexity increases
Solution Approach 1:
The system divides the complex indexing task into distinct functional modules: an extraction engine for processing raw data, a topic identification component for categorization, and an indexing mechanism for storage. This segmentation allows each component to handle specific aspects of data processing independently, reducing overall system complexity while maintaining automation capabilities
Solution Approach 2:
The patent introduces an intermediate extraction engine that acts as a mediator between raw data input and the final indexing system. This intermediary layer processes and structures data before it reaches the indexing mechanism, simplifying the overall architecture by creating a buffer zone that handles complex processing tasks separately
3Ease of operation
If users manually categorize information into appropriate directories, then data organization is achieved, but users may not fully understand the semantics of information leading to improper placement
Solution Approach 1:
The extraction engine autonomously performs semantic analysis and topic identification without requiring user knowledge of classification schemes. The system self-determines the appropriate categories by analyzing message content, sender relationships, and contextual patterns, eliminating the need for users to understand complex categorization semantics
Solution Approach 2:
The patent replaces human semantic judgment with automated text analysis algorithms. The extraction engine uses computational methods to identify topics and determine appropriate categorization, substituting human cognitive processes with machine-based semantic analysis that consistently applies classification rules
4Productivity
If traditional folder-based systems are used for email management, then basic sorting is possible, but tasks become invisible and easily neglected, and messages cannot serve multiple activities simultaneously
Solution Approach 1:
The system allows messages to simultaneously belong to multiple topics and categories rather than being confined to a single folder. A single message can be indexed under multiple extracted topics, enabling it to serve multiple retrieval activities and user needs simultaneously, increasing the utility and visibility of task-related information
Solution Approach 2:
The patent transitions from a single-dimensional folder hierarchy to a multi-dimensional topic-based indexing system. Messages are organized along multiple semantic dimensions simultaneously, allowing users to retrieve information through various topic pathways and making tasks visible across different contextual dimensions rather than being hidden in nested folder structures
Data Source
AI summary
A system for clustering events includes an extraction engine configured to receive message data from managed infrastructure that includes managed infrastructure physical hardware that supports the flow and processing of information. The managed infrastructure is associated with produced events that relate to it. Those events are converted into words and subsets used to group the events that relate to failures or errors in the managed infrastructure, including the managed infrastructure physical and virtual hardware and software. A sigalizer engine and a compare and merge engine are included.