Abstract: An intelligent forward error correction (iFEC) method and system are automatically triggered and started for providing forward error correction for applications when packet loss is predicted to be more than a threshold packet loss level during a prescribed interval and the actual packet loss is more than a threshold level. Similarly, the intelligent FEC method and system may be automatically disabled when packet loss is predicted to be less than the threshold level during a prescribed interval. If prescribed bandwidth of a transmission uplink through which application data packets will pass will be slowed or choked by use of iFEC, then iFEC may be disabled or stopped to prevent further congestion in the uplink. That is, according to examples, iFEC is started and stopped based on predicted packet loss and uplink/bandwidth health as opposed to simply operating forward error correction in a “always on” or manually provisioned manner.

Abstract: An intelligent forward error correction (iFEC) method and system are automatically triggered and started for providing forward error correction for applications when packet loss is predicted to be more than a threshold packet loss level during a prescribed interval and the actual packet loss is more than a threshold level. Similarly, the intelligent FEC method and system may be automatically disabled when packet loss is predicted to be less than the threshold level during a prescribed interval. If prescribed bandwidth of a transmission uplink through which application data packets will pass will be slowed or choked by use of iFEC, then iFEC may be disabled or stopped to prevent further congestion in the uplink. That is, according to examples, iFEC is started and stopped based on predicted packet loss and uplink/bandwidth health as opposed to simply operating forward error correction in a “always on” or manually provisioned manner.

Abstract: An approach is provided in which the approach trains a machine learning model using reference entries included in a reference dataset. During the training, the machine learning model learns a first set of unidirectional associations between the reference entries. The approach inputs a user dataset into the trained machine learning model and generates a second set of unidirectional associations between user dataset entries included in the user dataset. The approach builds a hierarchical relationship of the user dataset based on the second set of unidirectional associations and manages the user dataset based on the hierarchical relationship.

Abstract: An approach is provided in which the approach trains a machine learning model using reference entries included in a reference dataset. During the training, the machine learning model learns a first set of unidirectional associations between the reference entries. The approach inputs a user dataset into the trained machine learning model and generates a second set of unidirectional associations between user dataset entries included in the user dataset. The approach builds a hierarchical relationship of the user dataset based on the second set of unidirectional associations and manages the user dataset based on the hierarchical relationship.

Abstract: A method of and system for performing pilot testing of a software program in an organization is carried out by collecting pilot testing data generated from a pilot testing of a software program run on one or more hardware assets in the organization, determining whether a sufficient amount of pilot testing data has been collected, and, when so, calculating one or more pilot test metrics from the collected data. The calculated pilot test metrics may then be compared to similar metrics in a target population to evaluate the software program.

Abstract: A method of and system for performing pilot testing of a software program in an organization is carried out by collecting pilot testing data generated from a pilot testing of a software program run on one or more hardware assets in the organization, determining whether a sufficient amount of pilot testing data has been collected, and, when so, calculating one or more pilot test metrics from the collected data. The calculated pilot test metrics may then be compared to similar metrics in a target population to evaluate the software program.

Abstract: A portable positive air filtration device including a respiration component and a harness component. The harness component being connected with the respiration component so as to secure the respiration component against the face of a user and covering the mouth and nostril of the user. One or more fans or air-moving components, one or more filters and at least one power supply provide filtered air to the user at a positive pressure. In some examples, the device includes a mask housing, a filter, a fan and an electronics control system. One or more batteries are provided for powering the device. The electronic control system can be connected with an electronic device for real time monitoring and control.

Abstract: In one embodiment, a real-time data analysis system 160 may efficiently alert a service technician 170 about any service outages for a network service 120. The real-time data analysis system 160 may process a service signal 410 from an application interacting with a network service 120. The real-time data analysis system 160 may determine that the service signal 410 crosses a failure threshold 430 indicating an emergency event. The real-time data analysis system 160 may send an emergency alert about the emergency event.

Abstract: Creating web application using cloud-based friction-free databases without web hosting knowledge. User credentials are received at a web application service for initiating a web application task via client software. The user credentials are analyzed to determine whether to grant the user permission to initiate the requested web application task. The web application task is initiated when the permission is granted. Data associated with the initiated web application task is received at the web application service for analysis of the data to host and render a web application based solely on received data provided without basis of web hosting knowledge.

Abstract: Creating web application using cloud-based friction-free databases without web hosting knowledge. User credentials are received at a web application service for initiating a web application task via client software. The user credentials are analyzed to determine whether to grant the user permission to initiate the requested web application task. The web application task is initiated when the permission is granted. Data associated with the initiated web application task is received at the web application service for analysis of the data to host and render a web application based solely on received data provided without basis of web hosting knowledge.