Abstract: This disclosure relates to a reconfigurable simulative and predictive digital assistant/platform for simulation of a multi-stage processing facility. The digital assistant generates and assembles digital representations of the individual physical processing stages and components of the multi-stage processing facility in a reconfigurable manner according to a set of configuration commands generated using user inputs in a graphical user interface. At least one of the digital representations include a reusable predictive model that is trained when the digital representation is generated by the digital assistant. The digital assistant further performs simulation of the multi-state processing facility “as is” or in alternative “what-if” scenarios by simulating the digital representations according to a set of timing signals in the set of configuration commands.

Abstract: Embodiments provide systems and methods for identifying and mitigating outlier network activity. In embodiments, network activity by a plurality of users may be monitored and, based on the monitoring, a plurality of data sets may be compiled. Each of the plurality data sets may include information representative of activity by the plurality of users. A network model representative of at least a portion of the network activity may be constructed based on one or more of the plurality of data sets. The network model may be evaluated against a set of rules to produce outputs that include at least one of: a set of classifications, a set of link metrics, and a set of communities. Decision engine logic may be executed against the outputs to identify outlier network activity. In response to identifying outlier network activity, operations to mitigate the identified outlier network activity may be executed.

Abstract: This disclosure relates to a reconfigurable simulative and predictive digital assistant/platform for simulation of a multi-stage processing facility. The digital assistant generates and assembles digital representations of the individual physical processing stages and components of the multi-stage processing facility in a reconfigurable manner according to a set of configuration commands generated using user inputs in a graphical user interface. At least one of the digital representations include a reusable predictive model that is trained when the digital representation is generated by the digital assistant. The digital assistant further performs simulation of the multi-state processing facility “as is” or in alternative “what-if” scenarios by simulating the digital representations according to a set of timing signals in the set of configuration commands.

Abstract: Embodiments of systems and methods for identifying and mitigating outlier network activity are disclosed. In embodiments, network activity by a plurality of users may be monitored and, based on the monitoring, a plurality of data sets may be compiled. Each of the plurality data sets may include information representative of activity by the plurality of users. A network model representative of at least a portion of the network activity may be constructed based on one or more of the plurality of data sets. The network model may be evaluated against a set of rules to produce outputs that include at least one of: a set of classifications, a set of link metrics, and a set of communities. Decision engine logic may be executed against the outputs to identify outlier network activity. In response to identifying outlier network activity, operations to mitigate the identified outlier network activity may be executed.