@inproceedings{65781e3e4f114385acaee56a3400c7f2,
title = "Demo abstract: Extrapolation from participatory sensing data",
abstract = "In this demo, a learning system, called Metis, is presented that extrapolates missing pieces in participatory sensing data. The work addresses the challenge of incomplete coverage in participatory sensing applications, where lack of complete control over participant mobility and sensing patterns may create coverage gaps in space and in time. Metis learns the underlying spatiotemporal patterns of the measured phe- nomenon from available incomplete observations, and uses these patterns to infer missing data. We describe the overall system design and demonstrate the system using data collected during the New York City gas crisis in the aftermath of Hurricane Sandy.",
author = "H. Liu and S. Gu and C. Pan and W. Zheng and S. Li and S. Hu and S. Wang and D. Wang and T. Amin and L. Su and Z. Xie and R. Govindan and A. Barnoy and T. Abdelzaher",
year = "2013",
doi = "10.1145/2517351.2517431",
language = "English",
isbn = "9781450320276",
series = "SenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems",
publisher = "Association for Computing Machinery",
booktitle = "SenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems",
address = "United States",
note = "11th ACM Conference on Embedded Networked Sensor Systems, SenSys 2013 ; Conference date: 11-11-2013 Through 15-11-2013",
}