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- W2895853227 abstract "In the literature, semantic annotation on spatial trajectories is not stretched out or refined well in a structured way, which makes it cannot represent rich context or activity semantics. In this paper, we present a Self-Describing Semantic Trajectories Model (SDSTM) with meta parts called Scenario and Scenario-Instance, and these parts are enforced in JSON, and can describe entities and their relationships in ubiquitous situations in a structured and self-describing way. Through our model, spatial data and their context and activity semantics can be fused into a whole structured data for easy analysis further." @default.
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- W2895853227 date "2018-07-01" @default.
- W2895853227 modified "2023-09-26" @default.
- W2895853227 title "SDSTM: a Self-Describing Semantic Trajectory Model for ubiquitous situations" @default.
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- W2895853227 doi "https://doi.org/10.1109/csit.2018.8486186" @default.
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