Abstract: An industrial data pipeline architecture described supports annotation of raw data streams into scalable and durable cloud-level storage. A dynamic data mapping process collects industrial data from data tags that have been annotated with metadata indicating that the tags are to be made available for visualization, and connects data from these tags to a data aggregator object of a node-level or edge-level visualization tool. The data aggregator object dynamically adjusts its data collection footprint to include the annotated data tags, which are dynamically associated with the physical layout of the industrial equipment in the visualization. Visualization objects for a specified plant configuration act as data context containers that can be added to a plant visualization panel, and allows the annotated tags to be selected to build visualizations of selected data items.

Abstract: A reactive buffering system for use in IIoT data pipelines dynamically adjusts data accumulation and delivery by a node of a pipeline based on aggregated downstream metrics representing current data processing latencies of downstream nodes. Based on these downstream performance metrics, a reactive node that adjusts the size of the next data batch to be sent to an adjacent downstream node. The nodes of the data pipeline are configured to support a request-response based handshaking protocol whereby the nodes that send data to downstream nodes maintain up-to-date performance level information from adjacent downstream nodes. With this performance information, together with pipeline priorities, the sending node (or reactive node) adjusts the transmission rate and intermediate buffering of data. In this way, the nodes of the pipeline can dynamically regulate interim data storage to avoid overwhelming the pipeline system with too much data during periods of high latency.

Abstract: An industrial data pipeline architecture described supports annotation of raw data streams into scalable and durable cloud-level storage. A dynamic data mapping process collects industrial data from data tags that have been annotated with metadata indicating that the tags are to be made available for visualization, and connects data from these tags to a data aggregator object of a node-level or edge-level visualization tool. The data aggregator object dynamically adjusts its data collection footprint to include the annotated data tags, which are dynamically associated with the physical layout of the industrial equipment in the visualization. Visualization objects for a specified plant configuration act as data context containers that can be added to a plant visualization panel, and allows the annotated tags to be selected to build visualizations of selected data items.

Abstract: A reactive buffering system for use in IIoT data pipelines dynamically adjusts data accumulation and delivery by a node of a pipeline based on aggregated downstream metrics representing current data processing latencies of downstream nodes. Based on these downstream performance metrics, a reactive node that adjusts the size of the next data batch to be sent to an adjacent downstream node. The nodes of the data pipeline are configured to support a request-response based handshaking protocol whereby the nodes that send data to downstream nodes maintain up-to-date performance level information from adjacent downstream nodes. With this performance information, together with pipeline priorities, the sending node (or reactive node) adjusts the transmission rate and intermediate buffering of data. In this way, the nodes of the pipeline can dynamically regulate interim data storage to avoid overwhelming the pipeline system with too much data during periods of high latency.