Event Decomposition System Using Semantic Clustering for Infrastructure Monitoring
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Solution Overview
Problem
Current systems for managing and organizing large volumes of digital communications, such as emails and network traffic, face challenges in effectively decomposing events from managed infrastructures and applying semantic meaning, leading to difficulties in filtering out spam and efficiently retrieving relevant information due to the complexity and dynamic nature of data.
Innovation Solution
A system that decomposes events from managed infrastructures by applying semantic meaning through lexical distance analysis, using engines to extract common characteristics and group events into clusters, thereby identifying failures or errors and supporting information flow and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional folder-based systems are used to organize digital communications, then information can be stored in structured locations, but retrieval efficiency deteriorates due to manual organization requirements and inability to handle massive volumes of data
Solution Approach 1:
The system enables self-service organization by automatically analyzing event data, extracting semantic meaning, and clustering events into categories without human intervention. The event decomposition system processes incoming communications, identifies patterns, and organizes them autonomously based on semantic analysis, eliminating the need for manual folder creation and file sorting while maintaining high retrieval efficiency
Solution Approach 2:
The system transforms the organization approach by changing from manual categorical sorting to automated semantic parameter-based clustering. By extracting semantic meaning and using lexical distance analysis, the system dynamically groups events based on their semantic parameters rather than predefined folder structures, enabling automatic adaptation to new information types and improving both productivity and reducing operational complexity
2Productivity
If automated directory creation is implemented to handle massive web-based information, then information organization improves, but semantic accuracy deteriorates because users may not fully understand the semantics of information
Solution Approach 1:
The system introduces an intermediary semantic analysis layer between raw event data and final classification. The event decomposition system extracts semantic meaning from unstructured data, analyzes lexical distances, and uses this intermediate semantic representation to accurately cluster events. This intermediary processing ensures that automated classification achieves high semantic accuracy by understanding the meaning behind the data rather than relying on superficial patterns
Solution Approach 2:
The system performs preliminary semantic analysis and meaning extraction before final classification occurs. By pre-processing events to extract their semantic content and represent them in a standardized format, the system prepares the data for accurate clustering. This preliminary action ensures that when classification happens, the semantic meaning is already captured and can be accurately utilized for precise categorization
3Measurement precision
If semantic meaning is applied to event decomposition, then event clustering accuracy improves, but computational complexity increases due to the need for lexical distance analysis
Solution Approach 1:
The system segments the complex semantic analysis task into distinct processing stages: event extraction, semantic meaning identification, lexical distance calculation, and clustering. By dividing the overall process into manageable segments, each handling a specific aspect of the analysis, the system reduces computational complexity at each stage while maintaining high overall accuracy. This segmentation allows for optimized processing at each step rather than attempting to perform all operations simultaneously
Data Source
AI summary
Methods and system are provided for decomposing events from managed infrastructures. The system decomposes events from a managed infrastructure and includes a first engine that receives data from a managed infrastructure which includes managed infrastructure physical hardware. The infrastructure physical hardware supports the flow and processing of information. A second engine determines common characteristics of events and produces clusters of events relating to the failure of 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 support the flow and processing of information. Events are produced that relate to the managed infrastructure. The 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 hardware. The events have textural context. Semantic meaning is applied to the textual context of the events. A change to a managed infrastructure physical hardware component is made.