Abstract: A knowledge model is derived from many different data sources, including activities of a person's mobile devices, physical location, and various media consumption habits. A graph may be built having various nodes representing concepts from the data sources and edges representing relationships between them. From the graph, various inferences may be made that can provide insight that could not otherwise be obtained. The knowledge model may be deployed as several services, including rich geolocation services, recommendation services, and other services. The services may be accessed through an application programming interface, which may be a paid service with various payment options.

Abstract: A knowledge model is derived from many different data sources, including activities of a person's mobile devices and various media consumption habits. A graph may be built having various nodes representing concepts from the data sources and edges representing relationships between them. From the graph, various inferences may be made that can provide insight that could not otherwise be obtained. The knowledge model may be deployed as several services, including rich geolocation services, recommendation services, and other services. The services may be accessed through an application programming interface, which may be a paid service with various payment options.

Abstract: Human behavior may be predicted by building a model of people's physical movements along with a model that represents their affinities to various ontological elements, and their relationships to other people. The model may include a graph of interrelated people, as well as their hobbies, interests, employment, and other elements. The model may be analyzed by simulating people's activities as those activities interact with real or hypothetical networks. The real networks may be used to verify the model by comparing measured parameters to predictive parameters derived from simulation to calibrate the models. A calibrated model may then be used with a modified or hypothetical network to analyze the effects of changes to the real network.

Abstract: Human behavior may be predicted by building a model of people's physical movements along with a model that represents their affinities to various ontological elements, and their relationships to other people. The model may include a graph of interrelated people, as well as their hobbies, interests, employment, and other elements. The model may be analyzed by simulating people's activities as those activities interact with real or hypothetical networks. The real networks may be used to verify the model by comparing measured parameters to predictive parameters derived from simulation to calibrate the models. A calibrated model may then be used with a modified or hypothetical network to analyze the effects of changes to the real network.

Abstract: A knowledge model is derived from many different data sources, including activities of a person's mobile devices and various media consumption habits. A graph may be built having various nodes representing concepts from the data sources and edges representing relationships between them. From the graph, various inferences may be made that can provide insight that could not otherwise be obtained. The knowledge model may be deployed as several services, including rich geolocation services, recommendation services, and other services. The services may be accessed through an application programming interface, which may be a paid service with various payment options.