Event Data

Real-Time Event Analytics Streaming

Event Analytics within the Iotics operating environment is defined as the processing of real-time, event-based data to create additional meaningful events.

The new events are fed back to digital data twins in the operating environment where they are stored in event logs and shared with followers and applications. Applications can display information in dashboards (visual analytics) or use the data to formulate predictions based on a model (predictive analytics). All of these forms of analytics are under the blanket term “event analytics”. The Iotics technology streams event analytics to enable the use of other systems.

Basic Event Analytics

A basic example of event analytics is checking a value within predefined limits (i.e. a river level). If the value is outside the range then an event is created and sent to the relevant digital data twin (i.e. a boat). Followers of the digital data twin of the boat will receive this new event and react act in real-time.

Mature Event Analytics

A complex example is whether a train has deviated from its planned schedule. This has knock-on effects not only for the passengers, but also for the maintenance crews waiting for it at the end of day. The analytics in this case are more intricate, needing the position of the train, its route, its priority, etc. The result of the processing is that multiple significant events are created and sent, one to the train twin (immediately it is known that it has deviated) and one each to the planned and actual depots.

Iotics Toolset

Integration
Digital Data Twin
Brokerage
Event Analytics
Automation

Integration

Integrate a data source from any endpoint. Integrations can be leveraged from the existing integration library or customized privately.

The data is contextualized to map to any system, industry or global standard, allowing scalability, development and growth without standard restrictions.

Digital Data Twin

A digital data twin is created from integrated internal or external data sources. It is an abstracted and virtualized version of any endpoint – asset, process, system, place or person – with access to all its data and controls throughout its entire life.

A digital data twin provides a single source of truth free from vendor lock-in and deployed in a secure, private Operating Environment.

Brokerage

Brokered, granular interactions between digital digital twins allow selective and permission-based sharing of data. These relationships enable the construction of digital ecosystems that securely flow data across corporate boundaries in real-time between digital twins. The interactions can be transient or permanent to mirror the requirements of digital ecosystem participants.

Brokered, secure data sharing across customer, supplier and partner boundaries is critical for business agility, scalability and new revenue realizations.

Event Analytics

Real-time event analytics stream across brokered interactions between digital twins. The out-of-the-box capability streams only meaningful event data without polling or storing petabytes of data. Digital twins can be configured to recognize significant events and respond in real-time.

Event analytics streaming eliminates the need for monolithic data lakes, optimizes networks without data flooding and offers machine learning and AI opportunities.

Automation

Based on semantic meta data, new endpoints are automatically found and created as digital twins in the operating environment and configured in the digital ecosystem. Programmatic and automated find-and-bind powers agile, flexible, extensible and scalable architecture for machine readability and AI opportunities.

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