9.1 Users Management and RBAC

9.2 Authentication and Security

9.3 Data Management and Data Model Handling 

9.3.1 Storage

9.3.2 Retention

9.3.3 Export/Import 

Resource Management

Import Data

9.3.4 Working with Data Model
9.3.5 Data Extraction

9.4 Control Center

9.4.1 License Entitlements

9.5 Platform Settings

9.5.1 Definitions

9.5.2 Preferences

9.5.3 About

Configuring RCABot and ML Models > Time Series Analysis

1. Introduction to vuSmartMaps™

2. Getting Started with vuSmartMaps™

3. Installing vuSmartMaps™

4. Configuring vuSmartMaps™

Observability Sources

Data Pipeline and Parser Configuration

Data Enrichment Techniques

Onboarding Applications and Business Journeys
Configuring RCABot and ML Models

Tuning Hyperparameters and Model Training

5. Observability Through vuSmartMaps™

Dashboards and Visualization 

Business and Operational Storyboard
RCA Storyboard

Alert Console and Correlation

Alerts and Notification

Rule-based and Dynamic threshold-based Alerts

Programmable Alerts

Notifications and Triggers

Alert Customization Notification

RCABot and ChatBot Interactions

Ved – Gen AI Bot

Comprehensive Reporting

Log Management and Analysis

Application Observability

Journey Observability

6. Understanding vuSmartMaps™

Platform Architecture and Key Services
Insights from Logs, Traces and Metrics
How domain-centric approach enables better RCA and ML insights?
Security and Compliance

7. Administering vuSmartMaps™

Dashboard Creation



Custom Panels

• UTM Visualization

• Matrix Visualization

• Insights

Alert Creation

Data Source Management

Report Generation

Data Onboarding and Instrumentation

Mobile Dashboard Configuration

9. Managing vuSmartMaps™

Users Management and RBAC

Authentication and Security

• Data Management and Data Model Handling 



• Export/Import 

• Resource Management

• Import Data

Working with Data Model
Data Extraction

• Control Center

License Entitlements

• Platform Settings




10. Glossary

11. Support and Troubleshooting

Time Series Analysis

It is an anomaly detection technique based on forecasting. We perform forecasting based on time series decomposition (signal=trend+seasonality+residual).

Classical decomposition is used here making it computationally fast and extremely lightweight compared to other methods.

Click on vuCoreML on the left tab and navigate to the Workspaces section.

The workspaces page shows a list of previously configured Workspaces. Click on the + icon to create a new Workspace.

You can now configure the workspace; the workspace comprises 5 major sections

  • Details
  • Sources & Schema
  • Signalizers
  • Storyboard 


Enter the Workspace Name, Description, and choose the Category as Time Series Analysis, and choose the Run Type as Online or Offline as per the requirement.

Note: Choose Run Type as Online for live data and Offline for third-party systems or files.

Click on Create to create the Workspace.

Sources & Schema

Once Workspace is created, you will be directed to the Schema page, where you can configure the schema which comprises of Journey section only. The schema is the place where you’ll have to define the business journey and its metrics. 

Metrics can be categorized into three types,

  • Lead Indicators (Business impacting metrics)
  • Operational Indicators (Application/Infra/Underlying metrics)
  • External Indicators (External metrics)

Note: You must categorize the metrics accurately because the incidents will be detected only for the lead indicators.


The journey will be the super-set of all metrics and components (i.e. you can think of this as a business journey). You can categorize the metrics at the journey level if they don’t specifically come under any particular component. 

You can now click Journey and add a new signal.

For each signal you will be adding, you’ll have to specify the data model and metric column in that model for this signal. Only metric columns in a data model are eligible to be indicators. 

You can use the listing option to specify the data model and respective metric column and then categorize that metric using the category listing option. Similarly, you can add other signals.

Click on Save and then click on Submit Schema.


After successfully submitting the Schema, click on the right arrow mark at the top right and you will be directed to the Signalizers page.

The signalizers page contains the list of metrics configured in the Schema page along with information on ML methods that will be running for the respective metrics.

If you want to change the hyper-parameters for a particular metrics ML method, you can click the edit button of the metrics ML method.

It will direct you to the hyper-parameter editing page. On completion of editing, you can click the update for it to override the default parameters.

Now you can either globally activate the signalizers or activate only specific metric signalizers as per requirement locally (at the action section of each metric listed on this page)

After activation, a pop-up will come up where you can click the Activate button.

After clicking, the Time Series Analyser will start creating the required pipelines. Once the pipelines are created.

Click the right arrow to go to the next section > Storyboards.


The storyboard contains insights into a Workspace. 

  • Anomaly Scoring: Every anomaly detected is scored between 0 to 1. A higher score implies the point is very far from the prediction, hence bad.
  • Anomaly Masking: When an anomaly is detected by the model, the model knows that it’s an anomaly and will mask it during further training. So predictions are not affected by past anomalies in the data.
  • Text Insights on Anamoly: The initial section gives an overview of the list of metrics configured and their roles and health.

  • Anomaly Detection

  • Auto Baselining: Provides the upper and lower threshold for a signal.

Further Reading:

  1. RCA Bot
  2. Offline RCA Bot
  3. Event Correlation


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