Abstract: Systems and methods that correlate among disparate pieces of synchronized data, collected from an “internal” data stream (e.g., history data collected from an industrial unit) and an “external” data stream (e.g., traffic data on network services). A process trend component that determines/predicts an outcome of an industrial process and facilitates diagnostics/prognostics of an industrial system. Accordingly, relations among various parameters can be discovered (e.g., dynamically) and proper corrective adjustments supplied to the industrial process. Such enables a tight control and short reaction time to process parameters, and for a modification thereof.

Abstract: Systems and methods that vary multiple data sampling rates, to collect sets of data with different levels of granularity for an industrial system. The data for such industrial system includes sets of data from the “internal” data stream(s) (e.g., history data collected from an industrial unit) and sets of data from an “external” (e.g., traffic data on network services) data stream(s), based in part on the criticality/importance criteria assigned to each collection stage. Each set of data can be assigned its own unique data collection rate. For example, a higher sample rate can be employed when collecting data from the network during an operation stage that is deemed more critical (e.g., dynamic attribution of predetermined importance factors) than the rest of the operation.

Abstract: Systems and methods that correlate among disparate pieces of synchronized data, collected from an “internal” data stream (e.g., history data collected from an industrial unit) and an “external” data stream (e.g., traffic data on network services). A process trend component that determines/predicts an outcome of an industrial process and facilitates diagnostics/prognostics of an industrial system. Accordingly, relations among various parameters can be discovered (e.g., dynamically) and proper corrective adjustments supplied to the industrial process. Such enables a tight control and short reaction time to process parameters, and for a modification thereof.

Abstract: Systems and methods that vary multiple data sampling rates, to collect sets of data with different levels of granularity for an industrial system. The data for such industrial system includes sets of data from the “internal” data stream(s) (e.g., history data collected from an industrial unit) and sets of data from an “external” (e.g., traffic data on network services) data stream(s), based in part on the criticality/importance criteria assigned to each collection stage. Each set of data can be assigned its own unique data collection rate. For example, a higher sample rate can be employed when collecting data from the network during an operation stage that is deemed more critical (e.g., dynamic attribution of predetermined importance factors) than the rest of the operation.

Abstract: Systems and methods that record information associated with proprietary processes in sufficient detail, to facilitate process optimization. A subscription and notification component enables tracking of various events, wherein each event can subscribe to a corresponding log (e.g., a diagnostic log, alarm log, and the like), and vice versa—to display desired items based on predetermined classifications. Moreover, access to both an internal data stream(s) and an external data stream(s) can be supplied via the subscription and notification component of the subject innovation.

Abstract: Systems and methods that manage resources and distribution thereof within an industrial system. Such an automated and dynamic allocation service can allocate resources from pools of resources available to the industrial system, and hence supply an efficient operation (e.g., adding/subtracting resources dynamically based on usage). A plurality of allocation rules and/or algorithms for resource types can be predetermined, and/or dynamically trained by the allocation service. The data employed for the industrial system includes sets of data from the “internal” data stream(s) (e.g., history data collected from an industrial unit) and sets of data from an “external” (e.g., traffic data on network services) data stream(s), based in part on the criticality/importance criteria assigned to each collection stage.

Abstract: Systems and methods that displays available relationships between internal and external data streams. A coordination component can collect and analyze both the “internal” data stream(s) and the “external” data stream(s) simultaneously, and a visualization component can present a form of a visual cue, on a collection of history data and network data. Accordingly, instead of merely storing data values as function of time, other non-time series correlation states can be employed dynamically to represent data to the user.