NDLI: iDiary: from GPS signals to a text-searchable diary

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iDiary: from GPS signals to a text-searchable diary

Content Provider	ACM Digital Library
Author	Sugaya, Andrew Rus, Daniela Sung, Cynthia Feldman, Dan
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.
Starting Page	1
Ending Page	12
Page Count	12
File Format	PDF
ISBN	9781450320276
DOI	10.1145/2517351.2517366
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Mobile device Text query Activity recognition Gps Semantic compression
Content Type	Text
Resource Type	Article

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