Dataspark, Pte. Ltd.

Amy Xuemei Shi-Nash

The Anh Dang

Deepak Jayakumaran

Thomas Martin Holleczek

Yunye Jin

James Christian Decraene

The Agh Dang

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.

Error factor and uniqueness level for anonymized datasets

Uniqueness level for anonymized datasets

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.

Transportation network monitoring using cellular radio metadata

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.

Knowledge model for personalization and location services

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.

Traffic prediction and real time analysis system

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.

Abstracted graphs from social relationship graph

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.

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Singapore, Singapore

Amy Xuemei Shi-Nash