Unified Analysis Platform for Cross-Ecosystem Event Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional approaches face challenges in effectively analyzing and responding to events across disparate ecosystems in a computing environment, leading to incomplete, inaccurate, or costly manual processes that often miss common root causes of issues like test failures or service timeouts.
Innovation Solution
The solution involves automatically clustering and correlating events across distinct ecosystems to identify actionable items and their responsible hierarchical product parameters, enabling proactive responses and efficient issue resolution through machine-learning based techniques and unified analysis platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to analyze events across disparate ecosystems, then human effort and time are required for analysis, but the processes become incomplete, inaccurate, and costly
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated machine learning-based system. The unified analysis platform uses ML models to automatically cluster, correlate, and analyze events across disparate ecosystems, substituting human effort with computational algorithms that provide both speed and accuracy.
Solution Approach 2:
The system enables self-service analysis where the unified analysis platform autonomously performs event clustering, correlation, and root cause identification without requiring manual intervention. The machine learning models automatically process events and generate insights, making the system self-sufficient in analyzing cross-ecosystem issues.
2Reliability
If manual analysis processes are used across ecosystems, then human resources are engaged, but the processes become costly
Solution Approach 1:
The patent replaces costly manual analysis processes with an automated machine learning system. The unified analysis platform uses ML algorithms to reliably identify issues and root causes across ecosystems, eliminating the need for expensive human resources while maintaining or improving detection completeness.
3Ease of operation
If events are analyzed in compartmentalized ecosystems, then each ecosystem can be analyzed independently, but common root causes across ecosystems are missed
Solution Approach 1:
The patent merges previously compartmentalized ecosystem analyses into a unified analysis platform. The system combines events from multiple disparate ecosystems and uses machine learning to correlate them, enabling the detection of common root causes that span across ecosystem boundaries while maintaining analytical simplicity through automation.
Solution Approach 2:
The unified analysis platform provides universal analysis capabilities that work across all disparate ecosystems simultaneously. The machine learning models are designed to handle multiple ecosystem types and event formats, enabling cross-ecosystem correlation and root cause identification through a single multi-functional system.
4Productivity
If automated machine-learning based techniques are used to cluster and correlate events, then response times are improved and human effort is reduced, but system complexity increases
Solution Approach 1:
The patent segments the complex analysis system into distinct functional modules within the unified analysis platform: event collection, ML-based clustering, correlation analysis, and actionable item identification. This modular segmentation manages system complexity by organizing machine learning techniques into discrete, manageable components that work together to achieve fast automated analysis.
Data Source
AI summary
The present subject matter discloses techniques to automatically cluster events generated by a plurality of distinct ecosystems deployed in a connected environment in relation to a product. In operation, the clustered events may be correlated, and one or more of the correlated events that trigger a response may be identified as an actionable item. Based on a correlation between the actionable items, a hierarchical product parameter responsible for the actionable item may be identified. The hierarchical product parameter is an operational factor in a hierarchy of operational factors associated with the product, where the hierarchy of operational factors span over the plurality of distinct ecosystems operating in relation to the product. Subsequently, one or more entities across the distinct ecosystems may be notified of the actionable item, to initiate a required action. Further, techniques to detect operational factors associated with the product deployed in the distinct ecosystems are disclosed.