A conceptual view on trajectories
Data & Knowledge Engineering
Mining Massive RFID, Trajectory, and Traffic Data Sets
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
MoveMine: mining moving object databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A hybrid model and computing platform for spatio-semantic trajectories
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
SeMiTri: a framework for semantic annotation of heterogeneous trajectories
Proceedings of the 14th International Conference on Extending Database Technology
SeTraStream: semantic-aware trajectory construction over streaming movement data
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Human activity recognition with trajectory data in multi-floor indoor environment
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Exploring pattern-aware travel routes for trajectory search
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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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).