EXEM | XAIOps

XAIOps

AI-based Intelligent IT Operation
Management Solution

XAIOps detects the real-time load status and abnormal patterns of the operating system through data collection and learning on various
IT infrastructure environments, including applications/databases/servers/networks/unstructured logs, etc. Moreover, it is an AI-based IT operation
intelligence solution that supports the whole IT operation. By providing proactive responses, XAIOps will help users to predict future failure situations.

XAIOps
  • Deep Learning-based Solution Optimized for Time Series
    Performance Data

  • Pro-Active Response to Failure with Intelligent Future Prediction

  • Integrated Anomaly Cause Analysis
    for Various IT Resources

  • Verified by Large Financial/Manufacturing/Distributing Clients

Features

XAIOps is built on machine/deep learning methodology, which enables real-time data collection and pattern learning for intelligent IT operations. This includes more accurate anomaly detection, load prediction, root cause analysis, and proactive alarm provisioning, all of which are integral to XAIOps' DNA.

  • Intelligent Integrated
    Operation Management
    Detection/prediction/analysis using
    the latest AI learning model
  • Massive Data Collection and Learning
    Provide flexible connectivity with AI models through real-time data collection and refinement
  • Anomaly Detection
    Real-time anomaly detection of
    structured/unstructured data
  • Load Prediction
    Accurate prediction of future
    situations via deep learning model
  • Event/Failure Prediction
    Pro-Active response by predicting failure
    at a specific point in the future
  • Causal/Correlation Analysis
    Analysis of similar patterns or correlations for anomaly detection situations
  • Root Cause Analysis
    Prompt root-cause for
    the failure situations
  • Intelligent Alarm
    Provide Smart Alerts tailored to dynamic load situations through AI learning

Monitoring Views

Integrated Monitoring

Integrated Monitoring

Through AI automated diagnosis, XAIOps provides a real-time integrated monitoring dashboard where users can check the entire IT system environment, including application services, instances such as WAS/DB, and infrastructure such as servers/networks.

Service/Instance View

Service/Instance View

Based on the AI anomaly detection model, XAIOps determines whether there is an anomaly in each system area (Service/Instance) in real time and provides an alarm according to the anomaly judgment level. Real-time monitoring results are offered visually as well.

AI Monitoring

AI Monitoring

AI Monitoring allows users to detect real-time failure based on collected IT operation data. When a failure occurs, XAIOps provides root cause of failure and detailed connection information through automatic inference/analysis.

Proactive Prediction

Proactive Prediction

A confidence interval (Base-line) is generated through past data learning, and when real-time observations fall outside the range, it will be detected as an anomaly. Proactive response is possible by predicting the situation 30 minutes to 1 hour into the future.

Anomaly Detection

Log Anomaly Detection

XAIOps detects abnormal log patterns in real time by learning on various types of unstructured log files (including Biz Log, Sys Log, Was Log, etc.). What’s more, it is possible to check log messages and analyze correlations.

Analysis Views

Load Prediction

Load Prediction

Short-term load prediction is an analytic function for forecasting key performance indicators in the short term: within one week into the future.

Long-term load prediction is designed for analyzing long-term key performance indicator forecasts from 1 to 12 months into the future and is mainly used for transaction volume and expected usage of system resources.

Performance Statistics

Performance Statistics

Performance Statistics is useful for analyzing anomaly detection, predictive model performance by setting a desired analysis period, or analyzing past pattern types. You can confirm the performance (accuracy) of your operating model and use it as a baseline for activities’ improvement.

Instance Association

Instance Association

When an instance failure of a specific server occurs, it is possible to prevent the expansion of failure in advance by analyzing in detail which indicators were caused and which systems and problems may occur in related calls due to the failure of the instance.

Service Analysis

Service Analysis

In the event of a specific application service failure, XAIOps provides trend analysis of key performance indicators that affected the failure and trace analysis of performance delay intervals and causes. Detailed connection tracking is also easily checked by linking call analysis between services.

Event Statistics

Event Statistics

With XAIOps, users can easily check the status of events by date and statistics by grade for the past week and analyze the types of events that have occurred based on the date of failure in detail.

Architecture

Customer Cases

  • Electricity

    Establishment of an AI-based Intelligent Integrated Monitoring System

    Adoption Background

    Efficient and stable system operation is required by adopting the latest AI-based technology to the core business.

    Benefits

    • In the event of an IT failure, rapid failure detection and prediction provides us with proactive response to failure.

    • With AIOps, analysis time of the cause of failure has been shortened to within 5 minutes after establishing a rapid
       response system.

    • Obtaining integrated analysis and data utilization plan by collecting various IT operation data.

    • Improved service satisfaction of employees through stable operation and pro-active response of the core business
      system.

  • Bank

    Establishment of an AI Detection/Prediction System

    Adoption Background

    There was a need to build an intelligent failure prediction system for proactive failure response.

    Benefits

    • Verification of anomaly detection and predictive system possibility by establishing various IT operation data
       collection/processing/analysis systems.

    • Establishment of a proactive failure response system through rapid failure detection and confirmation.

    • Reduction and clarification of communication between departments as a comprehensive analysis of causes of
       failure becomes possible.

    • With intelligent control, work concentration has been improved through efficient redeployment of IT operation personnel.

  • Bank

    Establishment of an Intelligent ICT Integrated Monitoring System

    Adoption Background

    There was a need to build an intelligent control system through enterprise-wide monitoring data integration.

    Benefits

    • Establishment of centralized performance and failure analysis response system through collection of various IT
       operation data.

    • With application of the latest Machine Learning/Deep Learning-based algorithms, prompt ICT operation control
       becomes possible through improving the accuracy of failure detection and cause analysis.

    • Establish a proactive failure response system through rapid failure detection and prediction.

    • Clarification of failure cause analysis, better and more efficient communication between development / operation
       departments.

Elevate Your IT Stability, with EXEM Solutions.