Abstract: Facilitating web page spectroscopy in a communications network is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise receiving first data that describes a first communication packet flow and second data that describes a second communication packet flow. The operations can also comprise training a model based on the first data and the second data, as a result of which the model is trained to detect respective behaviors represented by the first data and the second. Further, the operations can comprise extracting a common parameter from third data that describes a third communication packet flow and fourth data that describes a fourth communication packet flow based on the model.

Abstract: In one example, a processing system including at least one processor may obtain a first packet, determine a first tunnel identifier from a tunnel identifier field and a first source port identifier from a source port identifier field of the header of the first packet, and assign the first packet to a first flow. The processing system may further obtain a second packet, extract a first value from a tunnel identifier field and a second value from a source port identifier field of a header of the second packet, determine that the first value matches the first tunnel identifier and that the second value matches the first source port identifier, and assign the second packet to the first flow in response to the determining that the first value matches the first tunnel identifier and that the second value matches the first source port identifier.

Abstract: In one example, a processing system including at least one processor may obtain a first packet, determine a first tunnel identifier from a tunnel identifier field and a first source port identifier from a source port identifier field of the header of the first packet, and assign the first packet to a first flow. The processing system may further obtain a second packet, extract a first value from a tunnel identifier field and a second value from a source port identifier field of a header of the second packet, determine that the first value matches the first tunnel identifier and that the second value matches the first source port identifier, and assign the second packet to the first flow in response to the determining that the first value matches the first tunnel identifier and that the second value matches the first source port identifier.

Abstract: Concepts and technologies disclosed herein are directed to data harvesting for machine learning model training. According to one aspect of the concepts and technologies disclosed herein, a network data collection system can identify a target data source location from which to harvest data for a machine learning system to utilize during a machine learning model training process. The data can be associated with a plurality of mobile communications devices operating in communication with at least one base station of a mobile communications network that serves the target data source location. The network data collection system can collect the data and provide the data to the machine learning system. The machine learning system, in turn, can create a training data set for use during the machine learning model training process based, at least in part, upon the data.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for varying the aggregation periods for data flows relative to the values of the data contained in the flows. For instance, in one example, a method includes intercepting a first flow and a second flow traversing a communications network, assigning a first value to the first flow and a second value to the second flow, wherein the first value is higher than the second value, aggregating the first flow into a first database record according to a first aggregation period, aggregating the second flow into a second database record according to a second aggregation period that is longer than the first aggregation period, and storing the first database record and the second database record in a database.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for varying the aggregation periods for data flows relative to the values of the data contained in the flows. For instance, in one example, a method includes intercepting a first flow and a second flow traversing a communications network, assigning a first value to the first flow and a second value to the second flow, wherein the first value is higher than the second value, aggregating the first flow into a first database record according to a first aggregation period, aggregating the second flow into a second database record according to a second aggregation period that is longer than the first aggregation period, and storing the first database record and the second database record in a database.

Abstract: Concepts and technologies disclosed herein are directed to data harvesting for machine learning model training. According to one aspect of the concepts and technologies disclosed herein, a network data collection system can identify a target data source location from which to harvest data for a machine learning system to utilize during a machine learning model training process. The data can be associated with a plurality of mobile communications devices operating in communication with at least one base station of a mobile communications network that serves the target data source location. The network data collection system can collect the data and provide the data to the machine learning system. The machine learning system, in turn, can create a training data set for use during the machine learning model training process based, at least in part, upon the data.

Abstract: Concepts and technologies disclosed herein are directed to data harvesting for machine learning model training. According to one aspect of the concepts and technologies disclosed herein, a network data collection system can identify a target data source location from which to harvest data for a machine learning system to utilize during a machine learning model training process. The data can be associated with a plurality of mobile communications devices operating in communication with at least one base station of a mobile communications network that serves the target data source location. The network data collection system can collect the data and provide the data to the machine learning system. The machine learning system, in turn, can create a training data set for use during the machine learning model training process based, at least in part, upon the data.

Abstract: Facilitating web page spectroscopy in a communications network is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise receiving first data that describes a first communication packet flow and second data that describes a second communication packet flow. The operations can also comprise training a model based on the first data and the second data, as a result of which the model is trained to detect respective behaviors represented by the first data and the second. Further, the operations can comprise extracting a common parameter from third data that describes a third communication packet flow and fourth data that describes a fourth communication packet flow based on the model.

Abstract: Facilitating web page spectroscopy in a communications network is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise receiving first data that describes a first communication packet flow and second data that describes a second communication packet flow. The operations can also comprise training a model based on the first data and the second data, as a result of which the model is trained to detect respective behaviors represented by the first data and the second. Further, the operations can comprise extracting a common parameter from third data that describes a third communication packet flow and fourth data that describes a fourth communication packet flow based on the model.

Abstract: In one example, a processing system including at least one processor may obtain a first packet, determine a first tunnel identifier from a tunnel identifier field and a first source port identifier from a source port identifier field of the header of the first packet, and assign the first packet to a first flow. The processing system may further obtain a second packet, extract a first value from a tunnel identifier field and a second value from a source port identifier field of a header of the second packet, determine that the first value matches the first tunnel identifier and that the second value matches the first source port identifier, and assign the second packet to the first flow in response to the determining that the first value matches the first tunnel identifier and that the second value matches the first source port identifier.

Abstract: Concepts and technologies disclosed herein are directed to data harvesting for machine learning model training. According to one aspect of the concepts and technologies disclosed herein, a network data collection system can identify a target data source location from which to harvest data for a machine learning system to utilize during a machine learning model training process. The data can be associated with a plurality of mobile communications devices operating in communication with at least one base station of a mobile communications network that serves the target data source location. The network data collection system can collect the data and provide the data to the machine learning system. The machine learning system, in turn, can create a training data set for use during the machine learning model training process based, at least in part, upon the data.

Abstract: Facilitating web page spectroscopy in a communications network is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise receiving first data that describes a first communication packet flow and second data that describes a second communication packet flow. The operations can also comprise training a model based on the first data and the second data, as a result of which the model is trained to detect respective behaviors represented by the first data and the second. Further, the operations can comprise extracting a common parameter from third data that describes a third communication packet flow and fourth data that describes a fourth communication packet flow based on the model.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for varying the aggregation periods for data flows relative to the values of the data contained in the flows. For instance, in one example, a method includes intercepting a first flow and a second flow traversing a communications network, assigning a first value to the first flow and a second value to the second flow, wherein the first value is higher than the second value, aggregating the first flow into a first database record according to a first aggregation period, aggregating the second flow into a second database record according to a second aggregation period that is longer than the first aggregation period, and storing the first database record and the second database record in a database.

Abstract: A method and apparatus for providing a service for recording calls on packet networks such as Voice over Internet Protocol (VoIP) and Service over Internet Protocol (SoIP) networks are disclosed. In one embodiment, the service provider enables the customers or subscribers of the service to send a request to activate the service for recording calls, e.g., via a web access input, a touchtone signal, and/or in accordance with predefined preferences. Once activated, the service provider provides a method for announcing to the subscriber and/or at least one other participant that the call recording service has been invoked. The announcement is provided to solicit an acceptance from the at least one other participant of the recording of the call. If an acceptance is received to the recording, the service provider begins recording the call.

Abstract: This invention relates to a method and apparatus for specifying alternative phone numbers to be used as Incoming Calling Line Identification (ICLID) numbers. When calling from a remote telephone station, the caller may provide personalized information including a telephone number and corresponding indicia, and indicate the telephone number is to be used as the ICLID number. Further, the caller may provide a personal message along with the telephone number and its accompanying indicia. Advantageously, callers may identify themselves, or for the purposes of ICLID, with their home or business telephone number even when they are calling from a different telephone station.