Incident Detection via Temporal Analysis and ML Profiles
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Solution Overview
Problem
Large entities face challenges in efficiently and accurately identifying incidents that may have a significant business impact, as conventional systems require manual evaluation and lack sufficient information, leading to time-consuming and inefficient resource allocation.
Innovation Solution
The use of machine learning to evaluate historical data and analyze incoming incidents using temporal and textual analysis, generating profiles for systems and networks to determine the likelihood of a significant business impact, and prioritizing resources accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual evaluation of incidents is used, then resource allocation can be made, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical evaluation with an automated machine learning system that processes incident data, generates impact scores, and prioritizes incidents automatically. The system uses historical incident data, system profiles, and machine learning models to eliminate manual evaluation steps, thereby increasing productivity and reducing time loss.
2Measurement precision
If all incidents are evaluated in detail, then accurate resource allocation is achieved, but the complexity of the system increases
Solution Approach 1:
The patent transforms the complex evaluation process into a standardized parameter-based system. It uses incident impact scores, system criticality ratings, and prioritization metrics as key parameters. The machine learning model processes multiple input parameters to generate a unified impact score, simplifying the assessment while maintaining accuracy through quantitative measurement rather than complex qualitative analysis.
Solution Approach 2:
The evaluation system is segmented into distinct functional modules: incident data collection, historical data retrieval, profile matching, machine learning scoring, and prioritization. Each module handles a specific aspect of the evaluation, reducing overall system complexity while maintaining comprehensive assessment capability through modular design.
3Measurement precision
If insufficient information is available for incident evaluation, then the evaluation process is simpler, but the accuracy of identifying significant business impact incidents decreases
Solution Approach 1:
The system performs preliminary actions by collecting and storing system profiles, historical incident data, and service dependency information before incidents occur. This pre-established knowledge base enables accurate real-time evaluation when incidents happen, eliminating the need to gather information during the evaluation process and ensuring complete information availability for accurate detection.
Data Source
AI summary
Systems and arrangements for using temporal analysis to evaluate incidents to determine whether they are likely to cause a significant business impact are provided. Historical data may be analyzed to identify incidents having a significant business impact. The historical data associated with incidents having a significant business impact may be further analyzed to identify a time and/or date at which the incident occurred, as well as the particular system, or the like, impacted by the incident. Normal business hours associated with the system, or the like, may be retrieved and a profile may be generated for the system, or the like. An incident may be received and processed to identify a system, or the like, associated with the incident and profile may be retrieved. The incident data may be compared to the profile to determine whether the incident is likely to cause a significant business impact based, at least in part, on the date and/or time at which it occurred.