Contents

• Introduction
• Quickstart Guide
  • Getting Started
  • Installing the Evaluation Package
    • Prerequisites
    • Installation Steps
    • Using the ScaleOut Digital Twins UI
    • Using the REST Service
    • Starting and stopping Docker containers
    • Uninstalling the Evaluation Package
  • Build Your First Digital Twin Model
    • Install Digital Twin Templates
    • Implement the Model
  • Test Your Model
    • Test with the Workbench (C# and Java)
    • Test with the Model Development Tool
  • Deploy Your Model
    • Package Your Model
    • Log in to the Digital Twins UI
    • Deploy Your Model
    • Send a Test Message
  • Next Steps
    • Contact Our Sales Team
    • Expand Your Digital Twins Application
    • Give Us Feedback
• Overview of ScaleOut’s Digital Twin Model
  • What Is ScaleOut’s Digital Twin Model?
  • Using Digital Twins for Streaming Analytics
  • Using Digital Twins for Simulation
  • Aggregate Analytics with Digital Twins
  • Building a Hierarchy of Digital Twins
  • Using Object-Oriented Design Principles
  • Machine Learning
    • Overview of ML Techniques
    • Spike and Trend Change Detection
    • Anomaly Detection with Supervised Learning
  • Azure Digital Twins Integration
    • Overview
    • Nomenclature
    • Integration Features
  • Hosting Digital Twins Using In-Memory Computing
• ScaleOut Digital Twin Builder™ Software Toolkit
  • Overview
    • Development Process
    • Development Using the Workbench Environment
    • Software Architecture of the Digital Twin Model
    • State Object
    • Messages
    • Message Processor
    • Simulation Processor
    • Processing Context
    • Application Endpoint
    • Timers
    • Sample Programs
    • Next Steps
  • ScaleOut Digital Twins Workbench
    • Software Toolkit Workbench APIs
    • See also
  • API Libraries
    • .NET Core
    • Java
    • REST
  • Azure Digital Twins Model Generator Tools
    • Model Generator for Java Models
    • Model Generator for C# and Rules-Based Models
  • LoadTwinsFromCsv Utility
    • Selecting a Payload Type
• ScaleOut Model Development Tool™
  • Prerequisites
  • A Tour of the ScaleOut Model Development Tool
    • Overview
    • Background on Digital Twins
    • Installation
    • The Model Development Tool’s User Interface
  • Defining a Model
    • Overview of a Real-Time Digital Twin Model
    • Common Model Elements
    • Machine Learning Models
    • Rules-Based Models
  • Testing a Model
    • Using the Test Tab to Send Messages to Model Instances
    • Viewing Properties and Messages in Test Model
    • Using Single-Step Mode to Debug a Model
    • Testing Timers
    • Testing Simulation Rules
  • Running simulations with multiple models
    • Overview
    • Loading multiple models
    • Import instances from CSV files
    • Control the simulation pace and execution
    • Create aggregated metrics for the simulation
    • Sample models for multiple model simulation
  • Deploying a Model
    • Saving Progress and Packaging a Model
    • Uploading Your Model to the ScaleOut Digital Twins Service
    • Exploring a Summary as HTML
    • Machine Learning Models
  • Sending Messages to a Deployed Model
  • Model Samples
    • Machine Learning Model Sample
    • Rules-Based Model Sample
• ScaleOut Machine Learning Training Tool™
  • Prerequisites
  • Create and Train an ML.NET Machine Learning Algorithm
    • Supervised Machine Learning for Anomaly Detection using ML.NET
    • Walkthrough: Train an ML.NET Machine Learning algorithm
  • Import an Existing TensorFlow Trained Algorithm
    • Tensorflow Algorithm Requirements
    • Walkthrough: Import an Existing TensorFlow algorithm
  • Use a Trained Algorithm (ML.NET or TensorFlow) for C# Digital Twins
    • Making Predictions From a C# Digital Twin Model
    • Testing the Model’s Predictions Locally with Workbench
    • Deploying Trained Machine Learning Algorithms
  • Continuous Learning for Algorithms used in C# Digital Twins
    • Continuous Learning with ScaleOut Digital Twins
    • Gathering and Download of New Training Data
    • Incremental Continuous Learning
    • Manual Update of ML Algorithm
• Connectors
  • Overview
    • Messaging Connectors
    • Persistence Connectors
  • AWS IoT Core
    • Concepts
    • Creating an AWS IoT Core Connector
    • Sending a Test Message
  • Azure IoT Hub
    • Concepts
    • Creating an Azure IoT Hub Connector
    • Managing an Existing Azure IoT Hub Connector
    • Sending a Test Message
  • Kafka Connectors
    • Concepts
    • Azure Kafka
    • On-Premises Kafka
  • REST
  • Azure Digital Twins
    • Enabling Persistence for a Digital Twin Model
  • SQL Database Connector
    • Enable Persistence for a Digital Twin Model
  • DynamoDB Connector
    • Enable Persistence for a Digital Twin Model
  • Cosmos DB Connector
    • Enable Persistence for a Digital Twin Model
  • Message Recording Connector
    • Enable the message recording for a Digital Twin Model
• ScaleOut Digital Twin Message Recording
  • Overview
  • Recording Messages
    • Prerequisites
    • Procedure
    • Next Steps
    • See also
  • Replaying a Recording
    • Background
    • Prerequisites
    • Procedure
    • See also
  • Managing Recordings
    • Installing the Message Recording Management Tool
    • Message Recording Management Tool Reference
• Exporters
  • Overview
  • Power BI
    • Concepts
    • Configuring Power BI
• Alert Providers
  • Overview
    • Configuring Your Alert Provider
    • Sending Alerts
  • Configuring an Alert Provider
    • .NET Core Models
    • Java Models
    • Rules Engine Models
    • Supported Alert Providers
  • Sending an Alert
    • .NET and Java Models
    • Rules Engine Models
  • Splunk On-Call
    • Mandatory Parameters
    • Optional Parameters
    • Setting Up Splunk’s Integration with ScaleOut Digital Twins
    • Configuration File Sample
  • Slack
    • Mandatory Parameters
    • Optional Parameters
    • Setting Up Slack’s Integration with ScaleOut Digital Twins™
    • Configuration file sample
  • PagerDuty
    • PagerDuty + ScaleOut Integration Benefits
    • How it works
    • Requirements
    • Support
    • Integration setup
• ScaleOut Digital Twins™ UI Guide
  • Overview
  • Creating and Managing Digital Twin Models
    • Creating a Digital Twin Model
    • Managing Digital Twin Models
    • Examining a Digital Twin Model
  • Simulation
    • Starting a Simulation
    • Managing a Simulation
  • Connections
    • Creating a Connection
    • Managing an Existing Connection
  • Widgets
    • Creating Dashboard Widgets
    • Managing Widgets
  • Exports
    • Creating an Export
    • Managing an Existing Export
  • System Information
    • Inspecting System Status
  • API Keys
    • Creating an API Key
    • Managing and Deleting API Keys
  • Queries
    • Building Queries
    • Viewing Query Results
    • Managing Queries
  • User Accounts
    • Viewing Your Profile
    • Managing Your Account
  • Organizations
    • Creating an Organization
    • Managing an Existing Organization
  • Administrator Options
    • Organization
    • Deployments
    • Managing Users
    • Managing API Keys
    • AI Assistant Settings
    • UI Settings
    • On-Premises Configuration
    • Resync with Backend
  • Generative AI Features
    • Configuring Generative AI
    • Generating Queries and Widgets
    • Trend and Anomaly Detection
  • Machine Learning
    • Uploading a Machine Learning Algorithm
    • Managing Machine Learning Algorithms
• On-Premises Deployment using ScaleOut StreamServer®
  • Overview
    • Using the UI
    • Using APIs
  • Deploying ScaleOut Digital Twins™ components
    • Deploying the ScaleOut Digital Twins UI
    • Deploying the Host Agent on Windows
    • Deploying the Host Agent on Linux
  • Deploying Models with APIs
    • Deploying .NET Core Models
    • Deploying Java Models
  • Deploying Connectors
    • Deploying AWS IoT Core Connectors
    • Deploying Azure IoT Hub Connectors
    • Deploying Kafka Connectors