Automated Root Cause Discovery for Enterprise Application Metrics
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Solution Overview
Problem
Measuring and analyzing enterprise application performance across millions of data points is complex and prone to human error, making it difficult to identify root causes effectively, especially in time-sensitive scenarios where updates are frequent.
Innovation Solution
A system that uses automated analysis to identify root causes by defining metrics and filters, employing analytics services to compare active and control groups, and generating recommendations for improving user operations, such as reducing after-hours collaboration, through a user-friendly interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of enterprise application performance data is performed, then human context understanding is applied, but analysis accuracy and speed deteriorate due to the volume of millions of data points
Solution Approach 1:
The patent replaces manual mechanical analysis with automated machine-driven analysis systems. The system uses automated data collection, processing, and analysis mechanisms to handle millions of data points, eliminating the limitations of human analysis speed while maintaining or improving accuracy through systematic computational methods.
Solution Approach 2:
The patent introduces an automated analysis system as an intermediary between raw enterprise application data and actionable insights. This intermediary system automatically collects, processes, and analyzes performance data, root cause information, and user feedback to generate recommendations without requiring direct human analysis of the raw data.
2Productivity
If automated analysis systems are used to process millions of data points, then analysis speed improves, but system complexity increases
Solution Approach 1:
The patent divides the complex analysis system into distinct functional modules: data collection modules that gather performance metrics, root cause identification modules that analyze collected data, feedback processing modules that handle user input, and recommendation generation modules that produce actionable insights. This segmentation manages complexity by creating independent, specialized components.
Solution Approach 2:
The automated analysis system is designed as a universal platform that can analyze multiple types of enterprise application data across different applications and contexts. The system handles various data formats, performance metrics, and analysis scenarios through a unified architecture, reducing overall system complexity through multi-functionality.
3Measurement precision
If comprehensive data collection is performed across all enterprise application metrics, then measurement completeness improves, but data processing time increases
Solution Approach 1:
The patent implements preliminary data collection and preprocessing mechanisms that continuously gather and organize enterprise application performance data before analysis is needed. Data is collected, validated, and structured in advance, so when analysis is required, the system can quickly process pre-prepared data without time-consuming preparation steps.
Solution Approach 2:
The system maintains continuous data collection and processing operations, constantly gathering performance metrics and updating analysis results. This continuous operation ensures that comprehensive data is always available and current, eliminating the need for time-intensive batch processing while maintaining measurement completeness.
4Measurement precision
If frequent updates to performance analysis are implemented, then data currency improves, but computational resource consumption increases
Solution Approach 1:
The patent implements periodic update cycles for performance analysis, where the system automatically refreshes data and re-runs analyses at scheduled intervals. This periodic action maintains data currency and relevance while managing computational resources by concentrating processing efforts in regular cycles rather than continuous high-intensity operations.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor data changes, user interactions, and performance thresholds to dynamically adjust update frequency. When significant changes occur or thresholds are breached, the system triggers immediate analysis updates. Otherwise, it operates on scheduled cycles, optimizing resource consumption while maintaining data currency through intelligent feedback-driven adaptation.
Data Source
AI summary
A system and method for determining enterprise metrics of an enterprise application is described. The system receives a root cause definition that identifies enterprise user metrics and predefined parameters for the enterprise user metrics. The enterprise user metrics identify operation metrics of the enterprise application by users of the enterprise. The system stores the root cause definition in a library of root causes definitions. The system receives a selection of a plan that identifies an operation attribute of the enterprise application. The system identifies a root cause from the library of root causes definitions based on the plan. The system generates a recommendation based on the identified root cause.