The Internet of Things (IoT) is about extracting and leveraging the tactical and strategic value of data. To do so it is essential that the sub-systems cooperating toward this goal agree on the meaning of data, and can share a "uniform" or at least an "equivalent" representation of data. Without a shared and agreed data model any IoT application quickly becomes an integration bazaar doomed to implode under the weight of layers of translations.
Additionally, data models, and in general data-centric architectures, provide a more stable abstraction-barrier than service APIs and service-oriented architectures – the failure of traditional Service Oriented Architectures (SOA) in favor of Resource-Oriented architectures is yet another example of this. Thus, for "data-intensive" systems that are expected to have a long life-cycle, and that require interoperability, such as IoT applications, data-centric architectures are the most natural choice.
The Data Distribution Service (DDS), is used as the data-management infrastructure at the foundation of many IoT applications, such as Smart Grids, Smart Transportation, Connected Vehicles and Smart Agriculture. One of the differentiating characteristics of DDS is its support for data- and resource-centric architectures.
This webcast will (1) explain the role and scope of data modeling in IoT – with references to the Reference Architecture Model Industrie 4.0 (RAMI 4.0) and the Industrial Internet Reference Architecture (IIRA), (2) introduce the techniques at the foundation of effective and extensible Data Models, and (3) explain how to design effective Data Models for systems powered by DDS.