An OT data lake, provides IIoT, OT, MES, and ERP data in dynamic context to intelligence applications in eXponenceTM. For expert users and data scientists, illuminator™ provides a fully integrated contextual data pipe to build AI applications with tools and libraries of their choice.
Qnnect™ is a complete, distributed edge and fog, hardware agnostic, software system for IIoT.
Edge Gateways (Qlite™) connect to sensor networks, PLC’s, and other edge devices using legacy and modern protocols. Fog nodes (QPro™) connect to multiple QLites to create a scalable, layered architecture aligned at each ISA95 level. QRelays™ may be used between levels to comply with ISA99/IEC62443 zones. Endpoint security and data encryption provide an additional layer of security. Distributed storage and compute allow execution of machine learning on Fog and Edge nodes.
QLite™ can be deployed on Edge appliances from Advantech, Dell, HPE and stratus. QPro™ can be deployed on appliances from Dell, HPE, Stratus or your Virtual Machines. Complete pre-configured appliances are also available from Quartic.ai
illuminator™ ingests data streams from multiple sources and protocols for a common reference.
This low-latency, high availability publish-subscribe data pipe provides access to real-time abstracted to build AI applications with eXponence™ and serves as an OT data Lake.
Advanced users and data scientists can connect with illuminator™, for contextual abstract data types to use machine learning tools and libraries of their choice and eliminate data integration needs to speed up deployment. Connectivity with illuminator™ is possible using Kafka Pub-Sub, illuminator API or SDK.
For a single process unit, site or the enterprise, AssetHarbor™ provides continuous extraction of context from multiple data streams.
Process operation, both batch and continuous are abstracted in the context of an asset. The illuminator™ data pipe then allows publish-subscribe access to the attributes of asset objects for building smart applications. Intelligence and insights created using MetaTrainer™, Reckon™ or external applications is added to the asset objects using extensible attributes. The reference data models can be custom, asset-oriented standards such as ISO14224 or manufacturing oriented such as ISA95.