Abstract: A dataset's uniqueness level may be calculated by analyzing a dataset to determine a uniqueness level. In cases where the uniqueness level may be too low for a particular purpose, meaning when the dataset may not provide enough anonymity, the dataset may be adjusted by recomputing the dataset with different resolutions of spatial data, temporal data, content data, and relationship data. By adjusting the resolution or accuracy of the data elements, the uniqueness level may thereby be adjusted. An error calculation may be determined by comparing the adjusted dataset to the original data, and the error value may represent the consistency of the data to the original data. The uniqueness level may be used as an assurance level of anonymity, which may be advertised when a dataset is sold or transferred to a third party for analysis.

Abstract: A dataset's uniqueness level may be calculated by analyzing a dataset to determine a uniqueness level. In cases where the uniqueness level may be too low for a particular purpose, meaning when the dataset may not provide enough anonymity, the dataset may be adjusted by recomputing the dataset with different resolutions of spatial data, temporal data, content data, and relationship data. By adjusting the resolution or accuracy of the data elements, the uniqueness level may thereby be adjusted. An error calculation may be determined by comparing the adjusted dataset to the original data, and the error value may represent the consistency of the data to the original data. The uniqueness level may be used as an assurance level of anonymity, which may be advertised when a dataset is sold or transferred to a third party for analysis.

Abstract: A transportation network monitoring system may use handoff metadata from cellular telephone and other communication networks to monitor train movement, traffic density, and traffic movement within the transportation network. Many communication technologies have handoff protocols that change a mobile device's connection from one base station to another. In a transportation network, such as a subway, a subway train may have several hundreds of riders, each of which may have a mobile device. As the train travels along the track, the handoff characteristics of those mobile devices may be analyzed to determine several characteristics of the transportation network, including the real time presence and speed of the train, as well as estimating the number of passengers and even the number of available seats on the train.

Abstract: A dataset's uniqueness level may be calculated by analyzing a dataset to determine a uniqueness level. In cases where the uniqueness level may be too low for a particular purpose, meaning when the dataset may not provide enough anonymity, the dataset may be adjusted by recomputing the dataset with different resolutions of spatial data, temporal data, content data, and relationship data. By adjusting the resolution or accuracy of the data elements, the uniqueness level may thereby be adjusted. An error calculation may be determined by comparing the adjusted dataset to the original data, and the error value may represent the consistency of the data to the original data. The uniqueness level may be used as an assurance level of anonymity, which may be advertised when a dataset is sold or transferred to a third party for analysis.

Abstract: A traffic routing and analysis system uses data from individual cellular or mobile devices to determine traffic density within a transportation network, such as subways, busses, roads, pedestrian walkways, or other networks. The system may use historical data derived from monitoring people's travel patterns, and may compare historical data to real time or near real time data to detect abnormalities. The system may be used for policy analysis, predicted commute times and route selection based on traffic patterns, as well as broadcast statistics that may be displayed to commuters. The system may be accessed through an application programming interface (API) for various applications, which may include applications that run on mobile devices, desktop or cloud based computers, or other devices.

Abstract: A system may generate abstracted graphs from a social relationship graph in response to a query. A query may identify a person for which permission has been obtains to collect their data. The abstracted graphs may include summary statistics for various relationships of the person. The relationships may include other persons, places, things, concepts, brands, or other object that may be present in a social relationship graph, and the relationships may be presented in an abstracted or summarized form. The abstracted form may preserve data that may be useful for the requestor, yet may prevent the requestor from receiving some raw data. When two or more people have given consent, the data relating to the consenting persons may be presented in a non-abstracted manner, while other data may be presented in an abstracted manner.

Abstract: A traffic routing and analysis system uses data from individual cellular or mobile devices to determine traffic density within a transportation network, such as subways, busses, roads, pedestrian walkways, or other networks. The system may use historical data derived from monitoring people's travel patterns, and may compare historical data to real time or near real time data to detect abnormalities. The system may be used for policy analysis, predicted commute times and route selection based on traffic patterns, as well as broadcast statistics that may be displayed to commuters. The system may be accessed through an application programming interface (API) for various applications, which may include applications that run on mobile devices, desktop or cloud based computers, or other devices.

Abstract: A system may generate abstracted graphs from a social relationship graph in response to a query. A query may identify a person for which permission has been obtains to collect their data. The abstracted graphs may include summary statistics for various relationships of the person. The relationships may include other persons, places, things, concepts, brands, or other object that may be present in a social relationship graph, and the relationships may be presented in an abstracted or summarized form. The abstracted form may preserve data that may be useful for the requestor, yet may prevent the requestor from receiving some raw data. When two or more people have given consent, the data relating to the consenting persons may be presented in a non-abstracted manner, while other data may be presented in an abstracted manner.

Abstract: A dataset's uniqueness level may be calculated by analyzing a dataset to determine a uniqueness level. In cases where the uniqueness level may be too low for a particular purpose, meaning when the dataset may not provide enough anonymity, the dataset may be adjusted by recomputing the dataset with different resolutions of spatial data, temporal data, content data, and relationship data. By adjusting the resolution or accuracy of the data elements, the uniqueness level may thereby be adjusted. An error calculation may be determined by comparing the adjusted dataset to the original data, and the error value may represent the consistency of the data to the original data. The uniqueness level may be used as an assurance level of anonymity, which may be advertised when a dataset is sold or transferred to a third party for analysis.

Abstract: A system may generate abstracted graphs from a social relationship graph in response to a query. A query may identify a person for which permission has been obtains to collect their data. The abstracted graphs may include summary statistics for various relationships of the person. The relationships may include other persons, places, things, concepts, brands, or other object that may be present in a social relationship graph, and the relationships may be presented in an abstracted or summarized form. The abstracted form may preserve data that may be useful for the requestor, yet may prevent the requestor from receiving some raw data. When two or more people have given consent, the data relating to the consenting persons may be presented in a non-abstracted manner, while other data may be presented in an abstracted manner.

Abstract: A traffic routing and analysis system uses data from individual cellular or mobile devices to determine traffic density within a transportation network, such as subways, busses, roads, pedestrian walkways, or other networks. The system may use historical data derived from monitoring people's travel patterns, and may compare historical data to real time or near real time data to detect abnormalities. The system may be used for policy analysis, predicted commute times and route selection based on traffic patterns, as well as broadcast statistics that may be displayed to commuters. The system may be accessed through an application programming interface (API) for various applications, which may include applications that run on mobile devices, desktop or cloud based computers, or other devices.

Abstract: A transportation network monitoring system may use handoff metadata from cellular telephone and other communication networks to monitor train movement, traffic density, and traffic movement within the transportation network. Many communication technologies have handoff protocols that change a mobile device's connection from one base station to another. In a transportation network, such as a subway, a subway train may have several hundreds of riders, each of which may have a mobile device. As the train travels along the track, the handoff characteristics of those mobile devices may be analyzed to determine several characteristics of the transportation network, including the real time presence and speed of the train, as well as estimating the number of passengers and even the number of available seats on the train.

Abstract: A traffic routing and analysis system uses data from individual cellular or mobile devices to determine traffic density within a transportation network, such as subways, busses, roads, pedestrian walkways, or other networks. The system may use historical data derived from monitoring people's travel patterns, and may compare historical data to real time or near real time data to detect abnormalities. The system may be used for policy analysis, predicted commute times and route selection based on traffic patterns, as well as broadcast statistics that may be displayed to commuters. The system may be accessed through an application programming interface (API) for various applications, which may include applications that run on mobile devices, desktop or cloud based computers, or other devices.

Abstract: A system for generating a user activity grid table is provided. The system includes processes for retrieving raw data from a server and processing this data to generate a user activity grid table that displays the timings that a user or a plurality of users spends in a particular geographical location, at a particular website or watching a particular channel.