Seeing Unified Namespace in Action and How to Implement UNS

When it comes to industrial automation and data integration, Unified Namespace (UNS) has emerged as a game-changer. It simplifies data flow across multiple systems, making it easier to access and utilize real-time information. In a recent webinar with HighByte, a provider of Industrial DataOps software solutions, we delved into how UNS was leveraged to power NeoMatrix’s Bracketology 4.0, a dynamic system built for tracking the predictions for the NCAA Division I men’s basketball single-elimination tournament of 68 teams using industrial data tools.

WATCH THE WEBINAR RECORDING: 

What is Unified Namespace?

See how It acts as a central hub where all data sources communicate, eliminating the traditional, inefficient, hierarchical data structures of Industry 3.0. This modern approach enables direct connections between systems, reducing complexity and increasing scalability.

Why UNS for Bracketology 4.0?

The goal was to create a system that would allow users to participate in a bracket prediction tournament using real-time game data. ESPN’s Rest API was used to fetch live scores, while industrial tools such as MQTT and Ignition were used to process and visualize the data efficiently. By implementing UNS, data movement across systems became streamlined, making it easier to manage and process predictions dynamically.

System Architecture Overview

The architecture of Bracketology 4.0 consists of multiple integrated components:

1. ESPN Rest API: The primary data source for game schedules, live scores, and team statistics.
2. HighByte: Used for structuring and distributing data via an integrated MQTT broker.
3. Ignition: Served as the front-end interface for users to submit predictions and track results.
4. SQL Database: Stored historical data, caching less frequently changing information to reduce Rest API calls.
5. Flow Software: Industrial DataOps solution for a Unified Analytics Framework used for processing user predictions and dynamically calculating scores based on live game data.

How UNS Was Implemented

1. Data Acquisition and Structuring
   HighByte connected to the ESPN Rest API, fetching real-time game data. It structured this data into a standardized format before publishing it to an MQTT broker, ensuring a consistent data flow for all connected systems.
2.  Data Distribution via MQTT
   The structured data was published through MQTT, allowing Ignition and Flow Software to subscribe to relevant topics. This facilitated real-time updates on game results and predictions without redundant Rest API requests.
3. User Interaction with Ignition
   Using Ignition’s Perspective module, a user-friendly interface was built for participants to select their bracket predictions. The system dynamically updated scores as games progressed, giving users real-time insights into their standing.
4. Data Processing with Flow Software
   Flow Software played a crucial role in computing scores based on users’ predictions and live game results. By leveraging UNS, it accessed structured data efficiently, ensuring accurate and timely score updates.

Key Benefits of Using UNS

• Scalability: The architecture easily accommodates additional data sources and components without significant rework.
• Real-time Data Flow: Eliminates latency in data processing, allowing instant updates for users.
• Vendor-Agnostic: Ensures compatibility across different platforms and technologies, making integration seamless.

Lessons Learned and Best Practices Implementing UNS

1. Define Data Structures Early: Predefining data models across all systems prevents inconsistencies and rework.
2. Use a Standardized Naming Convention: Following industry standards like ISA-95 enhances readability and scalability.
3. Start Small and Scale Gradually: Implementing UNS for a single system or production line helps mitigate risks before full-scale deployment.
4. Optimize Rest API Calls: Event-driven data requests minimize redundant queries, improving efficiency and reducing server load.

Unified Namespace has revolutionized data integration for Bracketology 4.0, providing a scalable, efficient, and real-time system for managing tournament predictions. By leveraging industrial data tools, we demonstrated how UNS can be applied beyond manufacturing, showcasing its versatility in handling complex, data-driven applications.