The data model forms the basis for standardizing data across a wide range of raw input data. A data model is comprised of a collection of attributes that are common to the logical instance. When working with industrial data, a data model is typically a standard representation of an asset, process, product, system, or role.
From one machine to the next, each industrial device may have its own data structure. Historically, vendors, systems integrators, and in-house controls engineers have not focused on creating data standards. They refined the systems and changed the data models over time to suit their needs. This worked for one-off projects, but today’s IIoT projects require more scalability. Leveraging standard models defined for your production lines, work cells, and machinery allows rapid adoption of analytics and visualization applications where individual datapoints do not need to be mapped and synchronized.
To handle the scale of hundreds of machines and controllers—and tens of thousands of data points—a set of standard models can be established using HighByte Intelligence Hub. The models correlate the data by machinery, process, and product and present it to the consuming applications. This systematic approach of building data models greatly accelerates the usage of this information and simplifies the management of the integrations. Using templates and parameters, a single modeled instance can represent hundreds of assets.
At the core of the model is the real-time data coming off the machinery and automation equipment. This data must often be augmented from many sources, including other equipment or controllers nearby, smart devices or sensors, derivations or transformations computed from existing data points available, metadata manually entered, and data from other databases or systems. Once the standard models are created in HighByte Intelligence Hub, they can be instantiated for each logical asset, process, and/or product. HighByte Intelligence Hub enables you to merge data from many sources.
Create models of assets, processes, products, and systems
Establish model hierarchy
Define models top-down or bottom-up
Normalize data to standard units of measure, scale, and types
Handle arrays of source data, model attributes, and sub-models
Model hundreds of common assets in minutes with templatized inputs and instances
Create models for AWS IoT SiteWise
Pass through fully modeled source data
Convert numeric enumerations to text value
Perform logic using multiple inputs to set attribute values
Leverage open models like ISA-95 or industry specific semantic model hierarchies
Import models from third parties, including DTDL models from Azure Digital Twins
