Quick Start: Three Typical Usage Scenarios

DFS provides three common workflows designed to help different user roles get started quickly:

1. Historical / Simulated Data Sources – for scenario playback and simulation.
2. General Datasets – for exploration, analytics, and visualization.
3. Real-Time Data Sources – for live production monitoring and control.

Scenario 1 — Simulation Using Historical or Simulated Data

Who it’s for:
Users who need to replay, demonstrate, or validate system behavior within a digital-twin environment.

Data Sources

• Historical data from equipment or scenes.
• User-uploaded simulated data files.

Typical Workflow

1. Build the digital-twin scene in Designer by modeling devices, environments, and interaction logic.
2. Import the scene into DFS.
3. Create a simulation task:

• Historical data task – Replays existing time-series data from devices or scenes (see Creating a Historical Data Source Simulation Task).
• Simulated data task – Uploads a mock data file to generate virtual device signals for testing or demos (see Creating a Simulated Data Source Simulation Task).

4. Run the task from the Task List to execute and play back data.
5. Drive the digital twin or scene by binding devices to their digital counterparts to verify motion and logic.

Note: Historical and simulated data sources are intended for playback and testing only. They do not generate general datasets and cannot be used directly for data exploration.

Result

• A fully replayable simulation task.
• Digital twins or scenes respond to recorded or simulated data for validation, testing, or presentation.

Scenario 2 — Exploration and Visualization with General Datasets

Who it’s for:
Users performing analytics, metric calculation, or dashboard visualization.

Data Sources

• Simulation logs automatically uploaded from Designer (converted to general datasets).
• Manually uploaded files in the General Datasets
• External enterprise data integrated via back-end connectors.
• Result datasets generated by prior data-exploration workflows.

Typical Workflow

1. Start a data exploration – Design a data-processing flow using cleaning, feature extraction, and computation nodes.
2. Publish the exploration method – When similar datasets are uploaded, the workflow executes automatically to generate result datasets.
3. Configure and publish dashboards – Visualize outputs that auto-refresh as new data arrives.

Note: General datasets are used for analytics and visualization only; they are not valid as historical-data sources.

Result

• A general dataset automatically produced after each simulation run.
• Automated execution of exploration workflows.
• Dashboards update dynamically, enabling simultaneous 3D playback and analytical insight—closing the loop from simulation to decision.

Scenario 3 — Real-Time Device Integration (Production Environment)

Who it’s for:
Users with physical equipment or control systems requiring live twin synchronization and monitoring.

Data Sources

• Real-time data streams from IoT devices, sensors, or enterprise systems.

Typical Workflow

1. Create an Adapter Instance – Configure data-source connections on the Adapter Instance page (see Creating an Adapter Instance).
2. Design a Node-RED flow – Define logic for data acquisition, cleansing, and forwarding (see Editing Node-RED Flows).
3. Bind devices to digital twins – Map incoming data fields to twin attributes (see Binding Devices and Digital Twins).
4. Run real-time visualization in Designer to verify live data response.
5. Monitor and operate via Designer or custom dashboards for live supervision and alerts.

Result

• A real-time, data-driven digital-twin environment.
• Continuous monitoring, maintenance, and alerting in live production settings.