Abstract: Systems and methods for key performance benchmarking may include receiving for a plurality of client devices of a tenant, a duration for performing a plurality of actions to log into a resource. The systems and methods can include determining metrics for each action of the plurality of actions. The systems and methods can include generating, by the one or more processors, one or more recommendations corresponding to at least one action of the plurality of actions, to reduce the duration to log into the resource.

Abstract: A method of troubleshooting an application includes receiving, from an analytics engine, data representing a performance metric of the application and a tenant identifier associated with the application; sending, to the analytics service, a request to receive at least one user identifier associated with the tenant identifier; receiving, from the analytics service, at least one user identifier; selecting, from a database, a message based at least in part on the performance metric of the application; and sending the message to the application associated with the at least one user identifier.

Abstract: Described embodiments provide systems and methods for intelligent load balancing of hosted sessions. A processor can determine a plurality of metrics for each of a plurality of machines configured to connect client devices with hosted sessions. The processor can receive, from a client device, a request to establish a connection with one of the plurality of machines to access a hosted session. The processor can determine a score for each of the plurality of machines based at least on the plurality of metrics for each of the plurality of machines. The processor can select a machine from the plurality of machines as a function of the score and a resource cost of the machine. The processor can cause the client device to connect to the selected machine for the hosted session.

Abstract: Systems, methods, and computer media for determining compatible users through machine learning are provided herein. Previous interactions between some users in a group can be used to determine a first set of user-to-user compatibility scores. Both the first set of compatibility scores and attributes for the users in the group can be provided as inputs to a machine learning model that can be used to determine a second set of user-to-user compatibility scores for user pairs who do not have an interaction history. Along with input constraints, the first and second sets of user-to-user compatibility scores can be used to select compatible user groups.

Abstract: A method may include collecting data from a variety of data sources associated with a user. The data sources may include personal data sources, corporate data sources, and public data source. The data collected from the variety of data sources may be enriched through categorization and aggregation. For example, browser history may be categorized based on types of website and aggregated to reflect the quantity of interactions with each category of website. A multi-dimensional digital profile may be generated based on the enriched data. For instance, the digital profile may include a social, emotional, spiritual, environmental, occupational, intellectual, and physical dimension. One or more recommendation corresponding to one or more of a burnout prediction, wellness recommendation, learning plan, skill gap, and personality type may be generated based on the digital profile. Related systems and computer program products are also provided.

Abstract: Described embodiments provide systems and methods for classifying a machine by performance. A device may identify, for a first time window, a first plurality of attributes of a machine and a session provided by the machine. The device may determine a first score based at least on a weight applied to each of the first plurality of attributes. The weight may be updated using a second plurality of attributes of the machine and the session provided by the machine for a second time window. The device may determine a probability of failure for the session by applying the first plurality of attributes to a model. The device may generate a second score indicating a performance of the machine as a function of the first score and the probability of failure. The device may classify the machine into a performance level in accordance with the second score.

Abstract: A server computer system configured to proactively predict a session failure of a virtual service is provided. The server computer system includes a memory and at least one processor coupled to the memory. The at least one processor is configured to receive one or more feature values associated with the virtual service. The processor can then evaluate a likelihood of session failure of the virtual service, such as a session launch failure, unresponsive state, or persistent session failure, in a future time interval based on the received feature values. The processor can then determine that the likelihood of session failure satisfies a classification test indicating the session failure is likely. Responsive to the determination, the processor can then execute a corrective operation, such as to end a user session, disable the virtual service, restart the virtual service, or render a user notification.

Abstract: A method may include training one or more machine learning models to predict a decline in employee performance. The machine learning models may be trained in a federated manner to avoid the exchange of personal data. The trained machine learning models may be applied to data associated with an employee that corresponds to one or more leading indicators of employee burnout. In response to the trained machine learning models predicting a decline in the performance of the employee, the root causes of the predicted decline in the performance of the employee may be identified by applying an explainability algorithm such as Shapley Additive Explanations (SHAP). A report including a corrective action for the predicted decline in employee performance may be generated based on the root causes. Related systems and computer program products are also provided.

Abstract: Systems and methods for scoring audio/video (A/V) sessions may include a first client which identifies an A/V signal for a session of an A/V application between the first client and a second client, and metrics of a network path between the first client and the second client. The first client may determine a first score for the A/V signal by applying one or more features corresponding to the A/V signal to a model trained to generate the first score. The client may generate a session score for the session based on the first score and the metrics of the network path.

Abstract: A method may include collecting data from a variety of data sources associated with a user. The data sources may include personal data sources, corporate data sources, and public data source. The data collected from the variety of data sources may be enriched through categorization and aggregation. For example, browser history may be categorized based on types of website and aggregated to reflect the quantity of interactions with each category of website. A multi-dimensional digital profile may be generated based on the enriched data. For instance, the digital profile may include a social, emotional, spiritual, environmental, occupational, intellectual, and physical dimension. One or more recommendation corresponding to one or more of a burnout prediction, wellness recommendation, learning plan, skill gap, and personality type may be generated based on the digital profile. Related systems and computer program products are also provided.