Abstract: Systems and methods for managing electronic resource access are disclosed. An example computer system may include: a processor; and a non-transitory memory storing one or more sets of instructions that when executed by the processor, causes the system to: receive a user request for accessing or modifying an electronic resource; process the user request to obtain text data; apply feature engineering to the text data to output a feature matrix, the feature engineering comprising application of natural language processing to the text data; use a trained machine learning model to determine a probability score indicating a likelihood of incident occurrence as a result of the user request; and generate signals for displaying a decision granting or denying the user request based on the probability score.

Abstract: A computational tool is proposed that is adapted as an automated batch impact evaluator (ABIE). The computational tool, for example, can be configured to monitor and track batch processing, generating computational outputs that can be surfaced to a user or downstream devices in the form of computer interface or application programming interface.

Abstract: An application outage on a computer network can be predicted by combining a utilization prediction of future application-level network utilization for an application executing on the network with an application health score for the application derived from multiple sources into a combined health score for the application. A threshold test can be applied to the combined health score, and remedial action may be initiated in response to the combined health score failing the threshold test. The remedial action may be, for example, an alarm and/or an automated correction procedure.

Abstract: A method for monitoring a network service based on a correlation including network traffic metrics experienced by the network service and infrastructure operational metrics of the network service, the method comprising the steps of: obtaining periodic data including the network traffic metrics, the infrastructure operational metrics, and social media metrics, the social media metrics including content associated with one or more services provided by the network service; storing the network traffic metrics, the infrastructure operational metrics, and social media metrics in a storage for use as historical data representing a predefined period of time; providing a correlation defining a relationship between metrics content of the periodic data; receiving the periodic data during operation of the network service and using the correlation to process the received periodic data to determine an output representing an infrastructure operational metric; comparing the infrastructure operational metric to a predefined o

Abstract: A method for monitoring a network service based on a correlation including network traffic metrics experienced by the network service and infrastructure operational metrics of the network service, the method comprising the steps of: obtaining periodic data including the network traffic metrics, the infrastructure operational metrics, and social media metrics, the social media metrics including content associated with one or more services provided by the network service; storing the network traffic metrics, the infrastructure operational metrics, and social media metrics in a storage for use as historical data representing a predefined period of time; providing a correlation defining a relationship between metrics content of the periodic data; receiving the periodic data during operation of the network service and using the correlation to process the received periodic data to determine an output representing an infrastructure operational metric; comparing the infrastructure operational metric to a predefined o

Abstract: Systems and methods are provided for detecting sub-optimal performance of one or more virtual computing platforms. Usage data representing user activity, and performance data representing computing hardware resource utilization, is collected from a plurality of virtual machines hosted on one or more virtual computing platforms. The usage data and performance data is then analyzed along with configuration data representing the hardware components of the computing devices operating the virtual computing platform.

Abstract: Systems and methods are provided for detecting sub-optimal performance of one or more virtual computing platforms. Usage data representing user activity, and performance data representing computing hardware resource utilization, is collected from a plurality of virtual machines hosted on one or more virtual computing platforms. The usage data and performance data is then analyzed along with configuration data representing the hardware components of the computing devices operating the virtual computing platform.