Low-performing cells, serving a telecom deployment in a given geographical area, are commonly identified by the telecom operators based on one or more Network KPIs computed for each of the cells being part of the deployment. However, most of these KPIs describe the overall state of affairs of the cell without any insight into the user usage profile of the cell. The present disclosure proposes a novel method to adjust the network KPIs based on scores of the macro cell or the small cell (101) with the user usage profile of the cell to obtain the user correlated network scores of the cells and prioritize the low-performing cells in the network in order of degree of impact on users and their usage. Another aspect of this disclosure is that it shall predict and compute a realistic user score to get predictive insights into user profiles for solutions and preventive measures.

Low-performing cells, serving a telecom deployment in a given geographical area, are commonly identified by the telecom operators based on one or more Network KPIs computed for each of the cells being part of the deployment. However, most of these KPIs describe the overall state of affairs of the cell without any insight into the user usage profile of the cell. The present disclosure proposes a novel method to adjust the network KPIs based on scores of the macro cell or the small cell (101) with the user usage profile of the cell to obtain the user correlated network scores of the cells and prioritize the low-performing cells in the network in order of degree of impact on users and their usage. Another aspect of this disclosure is that it shall predict and compute a realistic user score to get predictive insights into user profiles for solutions and preventive measures.

A method for configuring output of information from an IoT network provides an interface for receiving user inputs from a user, the user inputs representing an asset type. User created rules define an asset behavior. The rules are input by the user in a human-readable language. Information representing the asset type and asset behavior is associated with one or more physical devices associated with a node of the network. The asset type is associated with a virtual area, which is associated with a physical area. The physical devices report raw state data of the asset, which is converted into user-defined state data and displayed to the user.

Systems, apparatuses, and methods are provided herein for network incident management. A method for network incident management comprises aggregating network metrics associated with a monitored network in a historical network data database, identifying incidents based on the network metrics, generating a training data set based on the network metrics and the incidents, wherein the training data set comprises time series of network metrics as training input and incidents as labels, training an incident model using the training data set, receiving real-time network metrics from the network via the network interface, determining an incident prediction based on the incident model using the real-time network metrics as input, and causing a user interface device to provide an alert to a user based on the incident prediction.

Systems, apparatuses, and methods are provided herein for network incident management. A method for network incident management comprises aggregating network metrics associated with a monitored network in a historical network data database, identifying incidents based on the network metrics, generating a training data set based on the network metrics and the incidents, wherein the training data set comprises time series of network metrics as training input and incidents as labels, training an incident model using the training data set, receiving real-time network metrics from the network via the network interface, determining an incident prediction based on the incident model using the real-time network metrics as input, and causing a user interface device to provide an alert to a user based on the incident prediction.

A multi-tenant media processing platform system and method. At least a first media analysis service of a plurality of media analysis services is activated for at least a portion of an active communication session of an entity in the platform system. The first activated media analysis service performs a first media analysis on media of the active communication session that is collected by the platform system. The first activated media analysis service performs the first media analysis on the collected media while the communication session is active to generate a first media analysis result. During the active communication session, at least one media analysis result is applied.

A multi-tenant media processing platform system and method. At least a first media analysis service of a plurality of media analysis services is activated for at least a portion of an active communication session of an entity in the platform system. The first activated media analysis service performs a first media analysis on media of the active communication session that is collected by the platform system. The first activated media analysis service performs the first media analysis on the collected media while the communication session is active to generate a first media analysis result. During the active communication session, at least one media analysis result is applied.

Described herein are systems and methods for fault tolerance in a network cloud environment. In accordance with various embodiments, the present disclosure provides an improved fault tolerance solution, and improvement in the fault tolerance of systems, by way of failure prediction, or prediction of when an underlying infrastructure will fail, and using the predictions to counteract the failure by spinning up or otherwise providing new component pieces to compensate for the failure.

A method for remote monitoring includes (1) generating first sensor data from a first sensor at a first network node of a communications network and (2) sending the first sensor data from the first sensor to a second network node that is remote from the first network node, via the communications network. The first network node is powered from an electrical power grid that is separate from the communications network. The first sensor data may be raw sensor data and/or lossless sensor data.

A network administration device may include one or more processors to receive operational information regarding a plurality of network devices; receive flow information relating to at least one traffic flow; input the flow information to a model, where the model is generated based on a machine learning technique, and where the model is configured to identify predicted performance information of one or more network devices with regard to the at least one traffic flow based on the operational information; determine path information for the at least one traffic flow with regard to the one or more network devices based on the predicted performance information; and/or configure the one or more network devices to implement the path information for the traffic flow.