Convergence of the Lloyd Algorithm for Computing Centroidal Voronoi Tessellations
SIAM Journal on Numerical Analysis
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Location-based activity recognition
Location-based activity recognition
A framework for data quality and feedback in participatory sensing
Proceedings of the 5th international conference on Embedded networked sensor systems
Proceedings of the 7th international conference on Mobile systems, applications, and services
Streaming Algorithms for Line Simplification
Discrete & Computational Geometry
Activity-aware map: identifying human daily activity pattern using mobile phone data
HBU'10 Proceedings of the First international conference on Human behavior understanding
SeMiTri: a framework for semantic annotation of heterogeneous trajectories
Proceedings of the 14th International Conference on Extending Database Technology
On the semantic annotation of places in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
An effective coreset compression algorithm for large scale sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
The single pixel GPS: learning big data signals from tiny coresets
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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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.