The present disclosure provides novel solution for Network Optimization in telecommunications network that has traditionally always been driven by measuring and improving Key Performance Indicators (KPIs) of network elements vis-à-vis advancement for identification of the customers with poor experience and identification of spatial clusters of these customers to pinpoint the exact location of the problem, allowing for more targeted network optimization. The systems and methods contained in this invention enable the identification of these customers with poor experience and identifies spatial clusters of these customers to pinpoint the exact location of the problem, allowing for more targeted network optimization. This disclosure provides solution by aggregating a multitude of metrics pertinent to the user’s voice, data and coverage experience and deriving a single KPI, it is possible to benchmark and correspondingly track and improve their experience.

The present disclosure provides novel solution for Network Optimization in telecommunications network that has traditionally always been driven by measuring and improving Key Performance Indicators (KPIs) of network elements vis-à-vis advancement for identification of the customers with poor experience and identification of spatial clusters of these customers to pinpoint the exact location of the problem, allowing for more targeted network optimization. The systems and methods contained in this invention enable the identification of these customers with poor experience and identifies spatial clusters of these customers to pinpoint the exact location of the problem, allowing for more targeted network optimization. This disclosure provides solution by aggregating a multitude of metrics pertinent to the user’s voice, data and coverage experience and deriving a single KPI, it is possible to benchmark and correspondingly track and improve their experience.

A technology for assessing client experience in incident management can be implemented. The technology can fetch an event log entry from a first database comprising a plurality of event log entries generated by a client, wherein the event log entry is associated with a timestamp, an event descriptor, and a prescribed target time to close the event log entry. The technology can extract a communication message sent by the client from the event descriptor, determine a polarity score based on sentiment analysis of the communication message, determine a client experience index (CEI) based on the polarity score, save the CEI in an event record in a second database, determine an aggregated CEI based on an average of a plurality of CEIs determined for the corresponding plurality of event log entries, and output the aggregated CEI.

A technology for assessing client experience in incident management can be implemented. The technology can fetch an event log entry from a first database comprising a plurality of event log entries generated by a client, wherein the event log entry is associated with a timestamp, an event descriptor, and a prescribed target time to close the event log entry. The technology can extract a communication message sent by the client from the event descriptor, determine a polarity score based on sentiment analysis of the communication message, determine a client experience index (CEI) based on the polarity score, save the CEI in an event record in a second database, determine an aggregated CEI based on an average of a plurality of CEIs determined for the corresponding plurality of event log entries, and output the aggregated CEI.

The present application describes a network interface device that acts as an interface between a network service provided by a network service provider and one or more network access devices associated with a particular location and/or entity.

The present application describes a network interface device that acts as an interface between a network service provided by a network service provider and one or more network access devices associated with a particular location and/or entity.

A method and device for estimating a number of distinct subscribers of a telecommunication network impacted by network issues include, for a plurality of N successive counting periods preceding a current time, steps of determining, for each counting period, an estimate of a number of different subscribers impacted by at least one network issue by implementing a probabilistic counter structure, and storing at least one elementary counter in association to said counting period, aggregating the elementary counters in a multi-level final counter structure, wherein each level of the final counter structure has an associated probabilistic counter structure, the aggregation comprising, for each elementary counter: computing, for at least one level of the multi-level final counter structure, an intersection between said elementary counter and the probabilistic counter structure associated to said level of the multi-level final counter structure, and updating the multi-level final counter structure based on the intersection computed.

The present application describes a network interface device that acts as an interface between a network service provided by a network service provider and one or more network access devices associated with a particular location and/or entity.

The present application describes a network interface device that acts as an interface between a network service provided by a network service provider and one or more network access devices associated with a particular location and/or entity.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for resolving a contextual ticket. The methods, systems, and apparatus include actions of receiving a request from a user to generate a contextual ticket that indicates an issue with an integration, obtaining baseline information for the issue, generating, based on the baseline information, a ticket knowledge graph, providing the ticket knowledge graph to a machine-learning trained action determination engine, receiving, from the machine-learning trained action determination engine, an indication of an action for resolving the issue, and initiating, based on the indication of the action for resolving the issue, the action for resolving the issue.