Abstract: Disclosed herein identifies audiences of mobile devices that behave a like a seed group of devices. That is, the behave alike group are those devices that move in similar patterns and visit the similar locations with a similar frequency as the devices of the seed group. Similarity is based on correlative similarity in having visited matching categories of location styles identified via mapping data (e.g., devices that visit national parks at a similar frequency). Correlative similarity is performed using a machine learning model trained via a follow the regularized leader proximal.

Abstract: A method includes operating a device to affect a physical condition measured by a sensor during a training phase. In response to the sensor collecting a minimum number of data values during the training phase, the method includes transitioning to an operating phase and establishing an anomaly count baseline including an anomaly count limit using the data values collected during the training phase. The method includes operating the device to affect the physical condition during the operating phase including a time period over which anomalies for the device are counted. An anomaly includes sensor data from the sensor exceeding a statistical outlier limit and an anomaly count for the device includes a number of anomalies within the sensor data during the time period. The method includes performing an automated action based on whether the anomaly count for the device exceeds the anomaly count limit during the operating phase.

Abstract: Disclosed herein identifies audiences of mobile devices that behave a like a seed group of devices. That is, the behave alike group are those devices that move in similar patterns and visit the similar locations with a similar frequency as the devices of the seed group. Similarity is based on correlative similarity in having visited matching categories of location styles identified via mapping data (e.g., devices that visit national parks at a similar frequency). Correlative similarity is performed using a machine learning model trained via a follow the regularized leader proximal.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Disclosed herein identifies audiences of mobile devices that behave a like a seed group of devices. That is, the behave alike group are those devices that move in similar patterns and visit the similar locations with a similar frequency as the devices of the seed group. Similarity is based on correlative similarity in having visited matching categories of location styles identified via mapping data (e.g., devices that visit national parks at a similar frequency). Correlative similarity is performed using a machine learning model trained via a follow the regularized leader proximal.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Disclosed herein identifies audiences of mobile devices that behave a like a seed group of devices. That is, the behave alike group are those devices that move in similar patterns and visit the similar locations with a similar frequency as the devices of the seed group. Similarity is based on correlative similarity in having visited matching categories of location styles identified via mapping data (e.g., devices that visit national parks at a similar frequency). Correlative similarity is performed using a machine learning model trained via a follow the regularized leader proximal.

Abstract: Disclosed herein identifies audiences of mobile devices that behave a like a seed group of devices. That is, the behave alike group are those devices that move in similar patterns and visit the similar locations with a similar frequency as the devices of the seed group. Similarity is based on correlative similarity in having visited matching categories of location styles identified via mapping data (e.g., devices that visit national parks at a similar frequency). Correlative similarity is performed using a machine learning model trained via a follow the regularized leader proximal.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: Various embodiments of the present technology generally relate to data delivery. More specifically, some embodiments of the present technology relate to systems and methods for using spatial and temporal analysis to associate data sources with mobile devices. The delivery of data to support a wide variety of services for and about mobile devices that are based on data stored in corporate, commercial, and government databases which is not currently linked to individual mobile devices. Some embodiments allow advertisers to better target their ads to relevant target audiences with greater accuracy.

Abstract: A method is provided for a device comprising at least one sensor providing sensor data. The method includes receiving, by a computer coupled to the device, the sensor data, providing the sensor data to at least one rule detector, evaluating, by the at least one rule detector, instances of sensor data triggered by the at least one rule detector, and determining, by the computer, that a sampling interval has ended, and in response calculating a data quality index based on the instances of triggered sensor data.