Provides technology by detecting AI abnormalities and identifying the cause through prediction and analysis.
AI anomaly detection & prediction capabilities
Precision Cause Analysis Diagnostics
Different forms of analytical visualization
Data collection and learning capabilities
Event Alarms and Reporting Capabilities
Integrated, service-driven monitoring capabilities
It collects and classifies various indicators and logs collected from the ICT infrastructure in operation, stores big data, and provides failure prediction
and cause analysis through AI engines.
Flowchart of the AI data collection process of the PETAON Forecaster
Packaged Products of PETAON Forecaster
Detect possible anomalies in the ICT infrastructure, provide root causes and solutions to speed up problem resolution.
Anomaly detection capability
If the overall anomaly score exceeds the dynamic threshold, you can classify it as an anomaly event and visualize the detection results to view anomaly detection events.
Anomalies prediction
Once a precursor pattern is identified in the pattern of recent time series data, you can visualize the anomaly prediction event to see the anomaly prediction event.
- Provides anomaly prediction by self-learning rather than signature-based detection.
- Artificial intelligence-based anomaly detection and prediction function through machine learning such as deep learning
- Provide correlation analysis between analysis metrics
- Performance variation and prediction of pre-anomaly detection through system and service-specific correlation analysis
Provides individual dashboards dedicated to timeline-based analysis and helps synchronize historical data from widgets and facilitate hierarchical structured analysis.
Anomaly detection capability
Anomaly classification allows users to classify patterns in time series data, determine similarity to user-defined anomalous events, and visualize event types and probabilities.
Anomalies prediction
Once a precursor pattern is identified in the pattern of recent time series data, you can visualize the anomaly prediction event to see the anomaly prediction event.
- The ability of individual monitoring elements, such as independent data/equipment, to identify and provide cause logs through association with diagnostic failure events from log data arising from service and equipment operations.
Provides individual dashboards dedicated to timeline-based analysis, synchronizing historical data from widgets and allowing analysis through the Knowledge DB
Data collection capabilities
Data collection provides an interface for visualizing and setting up and managing collected data. The set data is collected from the collector in various ways, such as agents and SNMP. The analysis results can be confirmed through key artificial intelligence functions such as anomaly detection, anomaly classification, anomaly prediction, cause analysis, and response guides.
Provides a comprehensive history of real-time monitoring and AI events on one screen, and provides automatic reporting by scheduling reports.
Event comprehensive history management
- Alarms are largely divided into monitoring alarms and artificial intelligence alarms.
- The monitoring alarm consists of four types: Up, Unknown, Warning, Down/Critical, and the AI alarm is an alarm generated by abnormal detection and abnormal prediction of artificial intelligence.
- Alarms can also be communicated through various media such as UI/UX non-text (SMS) and email.
- Outputs a comprehensive history of real-time monitoring and AI events.
- Provides search capabilities to query specific nodes or specific event messages.
- It can be created with various extensions such as PDF, xls, hwp, html, etc.
- Schedule daily/weekly/monthly/annual reports and provide them for automatic generation.
- Generated reports are managed separately on the right side of each template and can be edited and deleted.
Provides intuitive awareness of events that occur by service group and customization of dashboards and widgets.
Service-centric monitoring
- Service-centric integrated monitoring visualizes the service topology based on logical connectivity information for all nodes that make up the service, rather than a single node, and service status, Monitoring capabilities that provide access within the overall context, such as the speed of response between the configuration nodes.
♦ Group Operations Widget
- Widgets representing events that occur/predicted around an operator-defined group of managed systems or services that can be visualized in different colors for user identification when events occur.
♦ Chart Widget
- Leverage the various types of chart (line, bar, pie, table, etc.) widgets available to help users organize and edit directly into widgets that specify, monitor critical indicators in their data.
♦ Timeline Widget
- Enables data at the time of event occurrence by switching to past event points around support events.
Variety of widgets, customizing, and intuitive integrated dashboards are available
Main Dashboard
- Add, delete, move, and resize widgets placed on the dashboard
- Provides basic placement configuration, edits dashboards to support management by user account group
Artificial Intelligence Dashboard
The AI dashboard allows users to see the results of anomaly detection and anomaly prediction at a glance through artificial intelligence functions.
