Providing architectural support for building context-aware applications
Providing architectural support for building context-aware applications
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Mobility prediction-based smartphone energy optimization for everyday location monitoring
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Context- and situation-awareness in information logistics
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
MoveSafe: a framework for transportation mode-based targeted alerting in disaster response
Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
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Finding the right data source for research is a challenge that many of us face. Although we live in times where 'Open Data' and 'Big Data' have become buzzwords, getting hold of a reasonable size and quality dataset is often hard. When it comes to user data such as mobility data, this becomes even tougher due to privacy-related concerns. This paper briefly explains our research in the area of multi-user context modeling and presents some criteria that we believe are important while selecting a dataset for testing different approaches in this domain. To find the right dataset, some relevant publicly available human mobility datasets are examined using these criteria. The following are the datasets that have been analyzed: Microsoft Research GeoLife Trajectory Dataset, Tracking Delft I Pedestrian Trajectory Dataset, MIT Media Lab Reality Mining Dataset and LifeMap Dataset. Besides these, some other useful data sources for researchers have been cited.