Contents

• Introduction
• Quickstart Guide
  • Getting Started
  • Installation
    • Overview
    • Installing the In-Memory Data Grid
    • Starting the UI’s Web Service
    • Installing the Provided Self-Signed Certificate
    • Opening the ScaleOut Digital Twins Management UI
  • 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
    • 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 with In-Memory Computing
• Installation and Configuration
  • Windows Installation
    • Prerequisites
    • Local or Remote
    • Start the ScaleOut Active Caching Web Service
    • Self-Signed And Trusted Certificates
    • First Time Log in
    • Installed Services and Components
  • Linux Installation
    • Prerequisites
    • Local or Remote
    • Start the ScaleOut Active Caching Web Service
    • Install Self-Signed Certificate
    • First Time Log in
    • Installed Services and Components
  • Certificate Installation
    • Introduction
    • Certificates
    • Securing ScaleOut Active Caching endpoints
  • Manage Users And Roles
    • First Time Login
    • Manage Users
• 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
  • Messaging Connectors
    • Configuration
    • SQS Connection
    • Azure IoT Hub
    • Kafka Connectors
    • REST
  • Persistence Providers
    • Configuration
    • Azure Digital Twins
    • SQL Database Connector
    • DynamoDB Connector
    • Cosmos DB Connector
  • Alert Providers
    • Configuration
    • Sending an Alert
    • Splunk On-Call
    • Slack
    • PagerDuty
• Exporters
  • Overview
  • Power BI
    • Concepts
    • Configuring Power BI
• 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
  • Shared Data
    • Accessing the Shared Data UI
    • Uploading Shared Data
    • Editing Shared Data JSON
    • Downloading Shared Data
    • Deleting Shared Data
  • Simulation
    • Starting a Simulation
    • Managing a Simulation
  • Connections
    • Creating a Connection
    • Managing an Existing Connection
  • Charts
    • Creating Dashboard Charts
    • Managing Charts
  • Exports
    • Creating an Export
    • Managing an Existing Export
  • System Information
    • Inspecting System Status
  • API Keys
    • Creating an API Key
    • Managing API Keys
  • Queries
    • Building Queries
    • Viewing Query Results
    • Managing Queries
  • User Accounts
    • Viewing Your Profile
    • Managing Your Account
  • Administrator Options
    • Managing Users
    • Managing API Keys
    • AI Assistant Settings
    • UI Settings
    • Resync with Backend
  • Generative AI Features
    • Configuring Generative AI
    • Generating Queries and Charts
    • Trend and Anomaly Detection
  • Machine Learning
    • Uploading a Machine Learning Algorithm
    • Managing Machine Learning Algorithms