Alert Dashboard System with Situation Room for Infrastructure Event Clustering
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Solution Overview
Problem
Current systems for managing and organizing vast amounts of messages/events from infrastructure, such as email and network communications, face challenges in clustering and filtering due to high volumes of unwanted messages like spam, and traditional folder-based systems are inefficient and not scalable.
Innovation Solution
A user interface system with engines that cluster events from managed infrastructure, identifying common characteristics and creating actionable problem clusters, allowing for a situation room with dashboards for managing and resolving issues within the infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional folder-based systems are used to organize messages, then messages can be stored in categorized folders, but it becomes impractical to handle and retrieve large volumes of messages efficiently
Solution Approach 1:
The patent replaces manual folder-based organization with automated text mining and natural language processing techniques. The system automatically extracts entities, events, and relationships from message content using computational linguistics algorithms, eliminating the need for manual categorization and enabling efficient retrieval through semantic search rather than hierarchical navigation.
Solution Approach 2:
The patent introduces an intermediary layer of text mining processing between message storage and retrieval operations. This intermediary automatically analyzes message content, extracts meaningful entities and relationships, and creates structured representations that enable rapid querying without requiring users to manually organize messages into folders.
2Adaptability or versatility
If manual directory creation is used to organize web-based information, then information can be hierarchically organized, but it is impractical to handle massive amounts of information and directories may not be optimally structured
Solution Approach 1:
The patent replaces manual directory creation and hierarchical organization with automated text mining systems that use natural language processing to understand content semantics. The system automatically extracts entities, relationships, and contextual information to create adaptive organizational structures based on the actual content rather than predetermined hierarchies.
Solution Approach 2:
The patent implements dynamic organization where the information structure adapts automatically based on content analysis. Rather than static hierarchical directories, the system creates flexible organizational structures that evolve with the content, using extracted entities and relationships to dynamically organize information in ways that reflect actual content relationships.
3Reliability
If rule-based spam filtering is used, then some spam can be detected, but spammers can circumvent detection by modifying content to evade filters
Solution Approach 1:
The patent replaces rigid rule-based filtering with adaptive text mining and natural language processing systems. Instead of checking against fixed patterns and keywords, the system uses computational linguistics to understand message semantics, entity relationships, and contextual meaning, making it difficult for spammers to evade detection through content modification.
Solution Approach 2:
The patent implements feedback mechanisms where the text mining system continuously learns from new spam patterns and evolves its detection capabilities. The system analyzes extracted entities, relationships, and contextual patterns to adapt to emerging spam techniques, creating a dynamic defense that improves over time rather than relying on static rules.
4Extent of automation
If centralized databases are used for spam signature maintenance, then spam detection can be standardized, but spammers can modify content to evade centralized filtering
Solution Approach 1:
The patent replaces centralized signature-based filtering with distributed text mining systems that analyze message semantics and contextual relationships. Instead of relying on centralized databases of known spam patterns, the system uses natural language processing to understand message meaning, entity relationships, and contextual cues, making evasion through content modification ineffective.
Solution Approach 2:
The patent creates a universal text mining framework that can detect various types of unwanted messages through semantic analysis rather than type-specific rules. The system extracts entities, events, and relationships that are applicable across different message formats and spam variations, providing a unified approach that maintains effectiveness against diverse spam techniques.
Data Source
AI summary
A user interface system includes a first engine configured to receive message data from managed infrastructure that includes managed infrastructure physical hardware that 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, where 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. One or more situations is created that is a collection of one or more events or alerts representative of the actionable problem in the managed infrastructure. A situation room includes a user interface (UI) for decomposing events from managed infrastructures. In response to production of the clusters one or more physical changes in a managed infrastructure hardware is made, where the hardware supports the flow and processing of information.