Abstract: The disclosed system identifies and resolves telecommunication network problems and/or problems with devices utilizing the network. The system segments the user devices into groups having similar characteristics, by grouping together devices more likely to leave the network, and devices less likely to leave the network. Each group of devices has a trained machine learning model, to predict the NEX score for each device. The system focuses on devices with a low NEX score. The system determines whether the problem is with the network and/or the problem is with the device. The system gathers information from network monitoring software and determine which tower caused, for example, the call to drop. The system determines technological capability of the device from the device TAC number. The system suggests solutions to the network problems by suggesting adding a sector, tuning an antenna, etc., or if the device has problems, adjusting phone settings.

Abstract: Embodiments include an interface processing system for displaying interrelated hierarchical building system models. The interface processing system can include memory and a processor allocation to load an executable asset from a data store to instantiate an instance of a building management service that can receive from a client application executing an operator interface on a client device, a request to display a visual representation of an asset located in a building. The service can query an asset database including structured data associated with the asset. Location data can be used to generate a digital model of a building system location associated with the asset. The building management service can retrieve structured model data for the asset and system level components and extract, from a set of asset schemas, a hierarchical relationship between the asset, locations and the system level components to generate an interactive visual representation of the building system location.

Abstract: Systems and methods discussed herein are directed to combining multiple network flows with respect to usage of applications into sessions. A record of a plurality of network flows may be obtained where the record relates to usage of the application by a user device in a wireless communication network. The plurality of network flows may be combined into a session, where an earliest start time of a network flow represents a session start time for the session and a latest end time of a network flow represents a session end time for the session. Based at least in part on the session start time and the session end time, a usage of the application during the session by the user device may be determined. Based at least in part on a plurality of sessions, usage of the application in the wireless communication network may be estimated.

Abstract: The disclosed system identifies and resolves telecommunication network problems and/or problems with devices utilizing the network. The system segments the user devices into groups having similar characteristics, by grouping together devices more likely to leave the network, and devices less likely to leave the network. Each group of devices has a trained machine learning model, to predict the NEX score for each device. The system focuses on devices with a low NEX score. The system determines whether the problem is with the network and/or the problem is with the device. The system gathers information from network monitoring software and determine which tower caused, for example, the call to drop. The system determines technological capability of the device from the device TAC number. The system suggests solutions to the network problems by suggesting adding a sector, tuning an antenna, etc., or if the device has problems, adjusting phone settings.

Abstract: The disclosed system identifies and resolves telecommunication network problems and/or problems with devices utilizing the network. The system segments the user devices into groups having similar characteristics, by grouping together devices more likely to leave the network, and devices less likely to leave the network. Each group of devices has a trained machine learning model, to predict the NEX score for each device. The system focuses on devices with a low NEX score. The system determines whether the problem is with the network and/or the problem is with the device. The system gathers information from network monitoring software and determine which tower caused, for example, the call to drop. The system determines technological capability of the device from the device TAC number. The system suggests solutions to the network problems by suggesting adding a sector, tuning an antenna, etc., or if the device has problems, adjusting phone settings.

Abstract: A computing system may automatically classify applications that are used via a communication network. Application classification may include identifying a signature or group of signatures that belongs to an application or service associated with data flow through a network. The computer system of the network may collect data regarding the application from a mobile device, from the network, and/or from a digital distribution service accessible via the network. The system may combine such data together to identify and classify the application.

Abstract: A system and method is disclosed to transform Over-The-Top (OTT) call log data collected by telecommunications network operators into a peer-to-peer log in order to determine more accurate information. In this method the IP addresses in the call log records are compared to a list of known mediator server IP addresses and the records in the log that contain mediator server IP addresses are removed from the log to create a peer-to-peer log containing only those records that contain peer IP addresses. The data in the peer-to-peer log can then be categorized with confidence to determine network performance information as well as, for example, the percentage of traffic to a particular country. Conclusions may then be inferred regarding the removed data records based upon the data found in the peer-to-peer log.

Abstract: Systems and methods discussed herein are directed to combining multiple network flows with respect to usage of applications into sessions. A record of a plurality of network flows may be obtained where the record relates to usage of the application by a user device in a wireless communication network. The plurality of network flows may be combined into a session, where an earliest start time of a network flow represents a session start time for the session and a latest end time of a network flow represents a session end time for the session. Based at least in part on the session start time and the session end time, a usage of the application during the session by the user device may be determined. Based at least in part on a plurality of sessions, usage of the application in the wireless communication network may be estimated.

Abstract: The disclosed system identifies and resolves telecommunication network problems and/or problems with devices utilizing the network. The system segments the user devices into groups having similar characteristics, by grouping together devices more likely to leave the network, and devices less likely to leave the network. Each group of devices has a trained machine learning model, to predict the NEX score for each device. The system focuses on devices with a low NEX score. The system determines whether the problem is with the network and/or the problem is with the device. The system gathers information from network monitoring software and determine which tower caused, for example, the call to drop. The system determines technological capability of the device from the device TAC number. The system suggests solutions to the network problems by suggesting adding a sector, tuning an antenna, etc., or if the device has problems, adjusting phone settings.

Abstract: A system and method is disclosed to transform Over-The-Top (OTT) call log data collected by telecommunications network operators into a peer-to-peer log in order to determine more accurate information. In this method the IP addresses in the call log records are compared to a list of known mediator server IP addresses and the records in the log that contain mediator server IP addresses are removed from the log to create a peer-to-peer log containing only those records that contain peer IP addresses. The data in the peer-to-peer log can then be categorized with confidence to determine network performance information as well as, for example, the percentage of traffic to a particular country. Conclusions may then be inferred regarding the removed data records based upon the data found in the peer-to-peer log.

Abstract: A computing system may automatically classify applications that are used via a communication network. Application classification may include identifying a signature or group of signatures that belongs to an application or service associated with data flow through a network. The computer system of the network may collect data regarding the application from a mobile device, from the network, and/or from a digital distribution service accessible via the network. The system may combine such data together to identify and classify the application.