Automatic Construction and Multi-level Visualization of Semantic Trajectories

With the prevalence of GPS-embedded mobile devices, enormous amounts of mobility data are being collected in the form of trajectory - a stream of (x,y,t) points. Such trajectories are of heterogeneous entities - vehicles, people, animals, parcels etc. Most applications primarily analyze raw trajectory data and extract geometric patterns. Real-life applications however, need a far more comprehensive, semantic representation of trajectories. This paper demonstrates the automatic construction and visualization capabilities of SeMiTri - a system we built that exploits 3rd party information sources containing geographic information, to semantically enrich trajectories. The construction stack encapsulates several spatio-temporal data integration and mining techniques to automatically compute and annotate all meaningful parts of heterogeneous trajectories. The visualization interface exhibits different levels of data abstraction, from low-level raw trajectories (i.e. the initial GPS trace) to high-level semantic trajectories (i.e. the sequence of interesting places where moving objects have passed and/or stayed).


Published in:
18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS)
Presented at:
18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), San Jose, CA, USA, November, 2010
Year:
2010
Keywords:
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 Record created 2010-11-04, last modified 2018-03-18

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