Evolving the Ecosystem of Personal Behavioral Data

Abstract

Everyday, people generate lots of personal data. Driven by the increasing use of online services and widespread adoption of smartphones (owned by 68% of U.S. residents; Anderson, 2015), personal data take many forms, including communications (e.g., e-mail, SMS, Facebook), plans and coordination (e.g., calendars, TripIt, to-do lists), entertainment consumption (e.g., YouTube, Spotify, Netflix), finances (e.g., banking, Amazon, eBay), activities (e.g., steps, runs, check-ins), and even health care (e.g., doctor visits, medications, heart rate). Collectively, these data provide a highly detailed description of an individual. Personal data afford the opportunity for many new kinds of applications that might improve people’s lives through deep personalization, tools to manage personal well-being, and services that support identity construction. However, developers currently encounter challenges working with personal data due to its fragmentation across services. This article evaluates the landscape of personal data, including the systemic forces that created current fragmented collections of data and the process required for integrating data from across services into an application. It details challenges the fragmented ecosystem imposes. Finally, it contributes Phenom, an experimental system that addresses these challenges, making it easier to develop applications that access personal data and providing users with greater control over how their data are used.

Notes

¹ https://www.google.com/settings/takeout

Additional information

Funding

Funding for this research comes from the Yahoo InMind project, The Stu Card Fellowship, A Google Faculty Research Award, NSF Grant No. DGE-0903659, and ONR N66001-12-C-4196. Any opinions, findings, and conclusions or recommendations are those of the authors and do not necessarily reflect those of any of the sponsors.

Notes on contributors

Jason Wiese

Jason Wiese ([email protected]) is a computer scientist. He is an Assistant Professor in the School of Computing at the University of Utah.

Sauvik Das

Sauvik Das ([email protected]) is a computer scientist. He is a Doctoral Candidate in the Human–Computer Interaction Institute at Carnegie Mellon University.

Jason I. Hong

Jason I. Hong ([email protected]) is a computer scientist. He is an Associate Professor in the Human–Computer Interaction Institute at Carnegie Mellon University.

John Zimmerman

John Zimmerman ([email protected]) is a design researcher. He is an Associate Professor in the Human–Computer Interaction Institute at Carnegie Mellon University.