@inproceedings{da9199b30b484a53b11e0d20be15b687,
title = "IDiary: From GPS signals to a text-searchable diary",
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 smart-phones 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 lo-cations 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.",
keywords = "Activity recognition, GPS, Mobile device, Semantic compression, Text query",
author = "Dan Feldman and Andrew Sugaya and Cynthia Sung and Daniela Rus",
year = "2013",
doi = "10.1145/2517351.2517366",
language = "English",
isbn = "9781450320276",
series = "SenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems",
publisher = "Association for Computing Machinery",
pages = "6:1--6:12",
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",
}