Event Clustering System With Feedback Signalizer Functor
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Solution Overview
Problem
Current systems for event clustering in managed infrastructure face challenges in efficiently organizing and retrieving messages/events due to the vast amount of information, lack of effective indexing, and the difficulty in handling spam and network traffic, leading to decreased worker productivity and network application slowdowns.
Innovation Solution
An event clustering system with a feedback signalizer functor that uses supervised machine learning and neural networks to replicate and create similar situations, grouping events into clusters based on common characteristics and features, and making physical changes in the managed infrastructure to support information flow and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual organization of messages/events into folders is used, then information can be categorized by topic, but the vast amount of information makes it impractical to manually store and retrieve messages efficiently
Solution Approach 1:
The system automatically organizes messages and events into clusters based on their content and characteristics without requiring manual user intervention. The clustering engine autonomously performs the organization task that would otherwise need to be done manually by users, making the system self-serving for the information organization function.
Solution Approach 2:
The patent replaces the mechanical manual process of sorting messages into folders with an automated computational clustering system. The clustering engine uses algorithms to automatically group related messages and events, substituting the manual mechanical action of file organization with an automated information processing system.
2Ease of operation
If traditional folder-based systems are used to organize messages, then messages can be sorted by topic, but tasks become invisible and easily neglected, and messages can only be in one folder at a time
Solution Approach 1:
The clustering system allows messages to belong to multiple clusters simultaneously, enabling a single message to serve multiple purposes and topics at once. This multi-functionality replaces the traditional single-folder constraint, allowing messages to be organized by multiple dimensions of relevance without being restricted to a single category.
Solution Approach 2:
The clustering system dynamically adjusts message groupings based on changing relevance and context, allowing clusters to evolve over time. This dynamic organization replaces static folder structures, enabling the system to adapt to changing user needs and message priorities, making tasks remain visible and actionable.
3Extent of automation
If automated directory creation is implemented, then massive amounts of web-based information can be handled, but the system complexity increases and requires sophisticated algorithms
Solution Approach 1:
The patent segments the automated organization task into distinct functional components: a clustering engine for grouping messages, an event processing system for handling infrastructure events, and a user interface layer. This segmentation allows the complex automated system to be managed through modular, independent components rather than a monolithic complex system.
Data Source
AI summary
An event clustering system that has an extraction engine in communication with a managed infrastructure. A signalizer engine includes one or more of an NMF engine, a k-means clustering engine and a topology proximity engine. The signalizer engine determines one or more common characteristics or features from events. The signalizer 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. A feedback signalizer functor is provided that is a supervised machine learning approach to train to reproduce a situation. 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.