AI Problem Description Generation for Enterprise Systems
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Solution Overview
Problem
Existing systems lack an efficient and automated method for generating problem descriptions from key performance indicators and operational metrics, particularly in large-scale enterprise systems, leading to delayed issue detection and resolution.
Innovation Solution
A system utilizing artificial intelligence technology, including a query component, learning component, and content component, generates problem descriptions from key performance indicators and operational metrics, and searches databases for recommendations, enabling real-time problem identification and solution provision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated problem description generation is implemented using AI technology, then productivity and response time are improved, but device complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: query component for receiving and parsing input, learning component for AI-based problem description generation, and content component for retrieving recommendations. Each module handles specific tasks independently, improving productivity while managing complexity through functional decomposition.
Solution Approach 2:
The learning component acts as an intermediary between raw query data and problem descriptions, using AI technology to bridge the gap between operational metrics and meaningful problem statements. This intermediary layer automates the transformation process without requiring direct complex interactions between all system components.
2Loss of time
If real-time problem identification is achieved through AI processing, then loss of time is reduced, but use of energy increases
Solution Approach 1:
The system performs preliminary processing by generating problem descriptions from key performance indicators and operational metrics before full-scale analysis is required. The learning component pre-processes data to create structured problem descriptions, enabling faster real-time response without requiring excessive computational energy during critical detection phases.
Solution Approach 2:
The query component identifies and focuses on a subset of key performance indicators that individually have performance below a threshold, rather than processing all available data. This partial action approach reduces energy consumption while still achieving real-time problem identification through targeted AI analysis of the most relevant metrics.
3Measurement precision
If comprehensive key performance indicators are analyzed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The query component extracts and identifies a specific subset of key performance indicators from the complete set of available metrics. By taking out only the relevant indicators that show performance below thresholds, the system achieves precise problem identification without the complexity of analyzing all possible metrics simultaneously.
Solution Approach 2:
Different components of the system handle different aspects of data analysis with specialized functions. The query component focuses on identifying relevant KPIs, the learning component specializes in generating problem descriptions from selected metrics, and the content component handles recommendation retrieval. This local quality specialization improves measurement precision while managing overall system complexity.
Data Source
AI summary
Techniques regarding providing artificial intelligence problem descriptions are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include, at least: a query component that generates key performance indicators from a query, determines a subset of key performance indicators that individually have a performance below a threshold, and maps the subset of key performance indicators to operational metrics; a learning component that generates, using artificial intelligence, problem descriptions from one or more of the subset of key performance indicators or the operational metrics and transmits the problem descriptions to a database.