Indian Journal of Science and Technology
Year: 2017, Volume: 10, Issue: 18, Pages: 1-14
Francisco Javier Moreno Arboleda1*, Santiago Román Fernández1 and Vania Bogorny2
1Facultad de Minas, Universidad Nacional de Colombia, Medellín, Colombia; [email protected], [email protected] 2Departamento de Informática e Estatística, Universidade Federal de Santa Catarina, Florianópolis, Brasil; [email protected]
*Author for the correspondence:
Francisco Javier Moreno Arboleda
Facultad de Minas, Universidad Nacional de Colombia, Medellín, Colombia; [email protected]
Objectives: To propose a new similarity function to determine trajectory similarity considering semantic aspects. Methods/Analysis: We propose different methods to calculate the similarity according to visited sites or activities performed: the first one considers only the sites included in the trajectories and the second considers the activities performed by the trajectories in the sites. A third method is proposed to find the similarity bitten trajectories based on both sites and activities. Findings: The similarity measure presented in this work allows us to make comparisons and user analysis according to trajectory data generated by users, which represents their routines, likes and preferences. This could be a key element for recommender systems, clustering or social networks. Novelty/Improvements: Our methods consider semantic aspects for finding the similarity of trajectories, considering visited sites and activities performed in these sites.
Keywords: Moving Objects, Semantic Trajectories, Similarity Measures, Trajectory Similarity
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