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: 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: A processor may apply data blocks of a training data set to a pattern matching algorithm to identify whether the data blocks match a pattern, determine points of divergence between the data blocks and the pattern, count a number of times that each of a plurality of positions in the pattern is determined to be a point of divergence, and determine a position with a highest count of a number of times that the position is determined to be a point of divergence. The processor may further receive an incoming data block, compare a data value at the position in the pattern with the highest count to a data value at a corresponding position in the incoming data block, and determine a mismatch when the data value at the position in the pattern and the data value at the corresponding position in the incoming data block are different.

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: A processor may apply data blocks of a training data set to a pattern matching algorithm to identify whether the data blocks match a pattern, determine points of divergence between the data blocks and the pattern, count a number of times that each of a plurality of positions in the pattern is determined to be a point of divergence, and determine a position with a highest count of a number of times that the position is determined to be a point of divergence. The processor may further receive an incoming data block, compare a data value at the position in the pattern with the highest count to a data value at a corresponding position in the incoming data block, and determine a mismatch when the data value at the position in the pattern and the data value at the corresponding position in the incoming data block are different.

Abstract: A processor may apply data blocks of a training data set to a pattern matching algorithm to identify whether the data blocks match a pattern, determine points of divergence between the data blocks and the pattern, count a number of times that each of a plurality of positions in the pattern is determined to be a point of divergence, and determine a position with a highest count of a number of times that the position is determined to be a point of divergence. The processor may further receive an incoming data block, compare a data value at the position in the pattern with the highest count to a data value at a corresponding position in the incoming data block, and determine a mismatch when the data value at the position in the pattern and the data value at the corresponding position in the incoming data block are different.

Abstract: A processor may apply data blocks of a training data set to a pattern matching algorithm to identify whether the data blocks match a pattern, determine points of divergence between the data blocks and the pattern, count a number of times that each of a plurality of positions in the pattern is determined to be a point of divergence, and determine a position with a highest count of a number of times that the position is determined to be a point of divergence. The processor may further receive an incoming data block, compare a data value at the position in the pattern with the highest count to a data value at a corresponding position in the incoming data block, and determine a mismatch when the data value at the position in the pattern and the data value at the corresponding position in the incoming data block are different.

Abstract: A femto cell service framework is employed for registration, activation, and provisioning of femtocell service. An account management service enables femtocell account creation, and registration thereof. Registration includes validation of wireless coverage for a location of operation of a femto AP, and validation of availability enhanced 911 (E911) service. As part of registration provisioning, record(s) are updated to indicate registration status. Connection of femto AP to a network interface triggers activation process; the femto AP delivers an activation request. A femto network validates registration of the connected femto AP and conducts a location tolerance check of a received actual location of the femto AP and a registered location. Upon tolerance validation, a cell identifier and radio frequency channel(s) are assigned to femto AP. When femto AP radiates wireless signal, network activation status indicators are provisioned. Handling of errors that arise during registration or activation is provided.

Abstract: A femto cell service framework is employed for registration, activation, and provisioning of femtocell service. An account management service enables femtocell account creation, and registration thereof. Registration includes validation of wireless coverage for a location of operation of a femto AP, and validation of availability enhanced 911 (E911) service. As part of registration provisioning, record(s) are updated to indicate registration status. Connection of femto AP to a network interface triggers activation process; the femto AP delivers an activation request. A femto network validates registration of the connected femto AP and conducts a location tolerance check of a received actual location of the femto AP and a registered location. Upon tolerance validation, a cell identifier and radio frequency channel(s) are assigned to femto AP. When femto AP radiates wireless signal, network activation status indicators are provisioned. Handling of errors that arise during registration or activation is provided.

Abstract: System(s) and method(s) are provided for a femto cell service framework for registration, activation, and provisioning of femtocell service. An account management service enables femtocell account creation, and registration thereof. Registration includes validation of wireless coverage for a location of operation of a femto AP, and validation of availability enhanced 911 (E911) service. As part of registration provisioning, record(s) are updated to indicate registration status. Connection of femto AP to a network interface triggers activation process; the femto AP delivers an activation request. A femto network validates registration of the connected femto AP and conducts a location tolerance check of a received actual location of the femto AP and a registered location. Upon tolerance validation, a cell identifier and radio frequency channel(s) are assigned to femto AP. When femto AP radiates wireless signal, network activation status indicators are provisioned.

Abstract: System(s) and method(s) are provided for a femto cell service framework for registration, activation, and provisioning of femtocell service. An account management service enables femtocell account creation, and registration thereof. Registration includes validation of wireless coverage for a location of operation of a femto AP, and validation of availability enhanced 911 (E911) service. As part of registration provisioning, record(s) are updated to indicate registration status. Connection of femto AP to a network interface triggers activation process; the femto AP delivers an activation request. A femto network validates registration of the connected femto AP and conducts a location tolerance check of a received actual location of the femto AP and a registered location. Upon tolerance validation, a cell identifier and radio frequency channel(s) are assigned to femto AP. When femto AP radiates wireless signal, network activation status indicators are provisioned.