Abstract
A major challenge in trajectory data analysis is the definition of approaches to enrich it semantically. In this paper, we consider machine learning and context information to enrich trajectory data in three steps: (1) the definition of a context model for trajectory domain; (2) the generation of rules based on that context model; (3) the implementation of a classification algorithm that processes these rules and adds semantics to trajectories. This approach is hierarchical and combines clustering and classification tasks to identify important parts of trajectories and to annotate them with semantics. These ideas were integrated into Weka toolkit and experimented using fishing vessel’s trajectories.
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Moreno, B., Júnior, A.S., Times, V., Tedesco, P., Matwin, S. (2014). Weka-SAT: A Hierarchical Context-Based Inference Engine to Enrich Trajectories with Semantics. In: Sokolova, M., van Beek, P. (eds) Advances in Artificial Intelligence. Canadian AI 2014. Lecture Notes in Computer Science(), vol 8436. Springer, Cham. https://doi.org/10.1007/978-3-319-06483-3_34
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DOI: https://doi.org/10.1007/978-3-319-06483-3_34
Publisher Name: Springer, Cham
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