Big Data for Ubiquitous Computing

The Carnegie Mellon project on Big Data for Ubiquitous Computing investigates how the use of Machine Learning and Data Mining can

1. Provide new insight into human behavior
2. Drive new application development
3. Create new kinds of activity recognition
4. Support the creation of new kinds of intelligent servies

This site will offer novel research and content around those themes -- including papers and demos -- developed by the Ubicomp Lab at Carnegie Mellon, and will highlight interesting research trends in the field, more broadly defined.

Under development is a RESTful API and GUI to our big datasets. These include

1. Family coordination
   Davidoff, S., Dey, A.K. & Zimmerman, J. (2010). How routine learners can support family coordination. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2010), 2461-2470.
2. Indoor locaation and movement
   From Christian Kohler and Dezhong Yao, upcoming.
3. Elderly activity dataset
   From Matthew Lee, upcoming.