Abstract: Analytics dashboards for critical event management systems that include artificial-intelligence (AI) functionalities, and related software. AI functionalities disclosed include pattern recognition and predictive modelling. One or more pattern-recognition algorithms can be used, for example, to identified patterns or other groupings within stored critical events, which can then be used to improve response performance and/or to inform the generation of predictive models. One or more predictive-modeling algorithms can be used to generate one or more predictive models that can then be used, for example, to make predictions about newly arriving critical events that can then be used, among other things, to provide optimal response performance and allow users to efficiently and effectively manage responses critical events. These and other features are described in detail.

Abstract: Analytics dashboards for critical event management systems that include artificial-intelligence (AI) functionalities, and related software. AI functionalities disclosed include pattern recognition and predictive modelling. One or more pattern-recognition algorithms can be used, for example, to identified patterns or other groupings within stored critical events, which can then be used to improve response performance and/or to inform the generation of predictive models. One or more predictive-modeling algorithms can be used to generate one or more predictive models that can then be used, for example, to make predictions about newly arriving critical events that can then be used, among other things, to provide optimal response performance and allow users to efficiently and effectively manage responses critical events. These and other features are described in detail.

Abstract: Analytics dashboards for critical event management systems that include artificial-intelligence (AI) functionalities, and related software. AI functionalities disclosed include pattern recognition and predictive modelling. One or more pattern-recognition algorithms can be used, for example, to identified patterns or other groupings within stored critical events, which can then be used to improve response performance and/or to inform the generation of predictive models. One or more predictive-modeling algorithms can be used to generate one or more predictive models that can then be used, for example, to make predictions about newly arriving critical events that can then be used, among other things, to provide optimal response performance and allow users to efficiently and effectively manage responses critical events. These and other features are described in detail.

Abstract: A computer-implemented process for an endpoint for automated fulfillment, includes: in an Automatic Fulfillment (AF) server: creating an endpoint on the AF server; selecting supported commands; and for each command, defining required and optional parameters and endpoint type specific implementation details; and in a Compliance Manager (CM): running discovery to find new endpoint and create metadata for it in the CM; for each command to be automated, filling in mappings for at least all required parameters; and enabling the command; enabling the endpoint; binding the endpoint to a business source; and ensuring that the business source uses a fulfillment process that uses the AF server.

Abstract: A computer-implemented process for an endpoint for automated fulfillment, includes: in an Automatic Fulfillment (AF) server: creating an endpoint on the AF server; selecting supported commands; and for each command, defining required and optional parameters and endpoint type specific implementation details; and in a Compliance Manager (CM): running discovery to find new endpoint and create metadata for it in the CM; for each command to be automated, filling in mappings for at least all required parameters; and enabling the command; enabling the endpoint; binding the endpoint to a business source; and ensuring that the business source uses a fulfillment process that uses the AF server.

Abstract: A method and apparatus is provided that enables accurate measurement of drop rate and delay in a System Under Test (SUT) by one or more monitoring devices even when the frame error rate of the monitoring devices may be imperfect. During a packet drop measurement process, ancillary information is identified and analyzed to determine if the ancillary information can be used to infer receipt of packets when explicit information regarding receipt is not present. A delay measurement process incorporates the time required to re-transmit packets into the delay measurement to more accurately reflect SUT operation.

Abstract: The present invention provides methods and system for organizing a dataset in a database by marking the dataset by a plurality of labels generated based on a pre-define policy. The policy determines the data scope accessible to each label. A user of the database can access the data within the scopes of one or more labels based on its role and privileges granted thereto by, for example, a system administrator. Moreover, a variety of shaping transformations can be applied to the tagged dataset to create a derived dataset that is suitable for the informational needs of the user. The derived dataset can be formatted to render it compatible for viewing via a selected presentation engine, such as a web browser.