iDiary: from GPS signals to a text-searchable diary
Name
Rus_IDiary.pdf
Size
2.99 MB
Format
Adobe PDF
Checksum (MD5)
797d10bec4266a78be53a91df8a08ffb
Author(s)
Feldman, Dan
Sugaya, Andrew
Sung, Cynthia Rueyi
Rus, Daniela L.
Date Issued
November 2013
Journal
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys '13)
Publisher
Association for Computing Machinery (ACM)
Citation
Dan Feldman, Andrew Sugaya, Cynthia Sung, and Daniela Rus. 2013. iDiary: from GPS signals to a text-searchable diary. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys '13). ACM, New York, NY, USA, Article 6, 12 pages.
Version
Author's final manuscript
Abstract
This paper describes a system that takes as input GPS data streams generated by users' phones and creates a searchable database of locations and activities. The system is called iDiary and turns large GPS signals collected from smartphones into textual descriptions of the trajectories. The system features a user interface similar to Google Search that allows users to type text queries on their activities (e.g., "Where did I buy books?") and receive textual answers based on their GPS signals.
iDiary uses novel algorithms for semantic compression (known as coresets) and trajectory clustering of massive GPS signals in parallel to compute the critical locations of a user. Using an external database, we then map these locations to textual descriptions and activities so that we can apply text mining techniques on the resulting data (e.g. LSA or transportation mode recognition).
We provide experimental results for both the system and algorithms and compare them to existing commercial and academic state-of-the-art. This is the first GPS system that enables text-searchable activities from GPS data.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2517351.2517366
