Introducing the products of MoaData AI technology.

Optimal AI anomaly
detection/prediction solutions for institutions and enterprises

PETAON Forecaster

Key features of PETAON Forcaster

Provides technology by detecting AI abnormalities and identifying the cause through prediction and analysis.

Single Solution Big data, artificial intelligence infrastructure management, related patents and software registration completed

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

PETAON Forecaster Configuration

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

01 AI anomaly detection & prediction

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

02 Anomaly classification and cause analysis capabilities

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.

03 Data collection and learning capabilities

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.

04 Alarm & Reporting Function

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.

05 Integrated, service-centric monitoring capabilities

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.

06 Integrated Dashboard

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.

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