Activity sensing in the wild: a field trial of ubifit garden
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Teenagers and their virtual possessions: design opportunities and issues
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 13th international conference on Ubiquitous computing
Mining smartphone data to classify life-facets of social relationships
Proceedings of the 2013 conference on Computer supported cooperative work
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Almost every computational system a person interacts with keeps a detailed log of that person's behavior. The possibility of this data promises a breadth of new service opportunities for improving people's lives through deep personalization, tools to manage aspects of their personal wellbeing, and services that support identity construction. However, the way that this data is collected and managed today introduces several challenges that severely limit the utility of this rich data. This thesis maps out a computational ecosystem for personal behavioral data through the design, implementation, and evaluation of Phenom, a web service that factors out common activities in making inferences from personal behavioral data. The primary benefits of Phenom include: a structured process for aggregating and representing user data; support for developing models based on personal behavioral data; and a unified API for accessing inferences made by models within Phenom. To evaluate Phenom for ease of use and versatility, an external set of developers will create example applications with it.