Multiple-Aspect Trajectory Similarity Measuring

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Multiple-Aspect Trajectory Similarity Measuring

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dc.contributor Universidade Federal de Santa Catarina pt_BR
dc.contributor.advisor Bogorny, Vania
dc.contributor.author May Petry, Lucas
dc.date.accessioned 2019-12-08T12:55:12Z
dc.date.available 2019-12-08T12:55:12Z
dc.date.issued 2017-11-06
dc.identifier.uri https://repositorio.ufsc.br/handle/123456789/202504
dc.description TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Ciências da Computação. pt_BR
dc.description.abstract The wide use of mobile devices such as GPS, as well as the popularization of social media, has led to the generation of large amounts of movement data, called trajectories of moving objects. Trajectory data analysis and mining has become very important because of the variety of information that may be extracted/inferred from these data, such as the daily habits or the profile of individuals. Because of the complexity of the data, they must be analyzed not only from the spatial and temporal characteristics, but any other semantics that may be related to the data. Behind the large amount of information available about movement, trajectories may be analyzed from multiple points of view, that we call multiple aspect trajectories. Similarity measures are widely employed for trajectory data analysis and have a large impact on the analysis outcomes. Most existing works for trajectory similarity are limited to the space and time dimensions of trajectories, and only a few analyze some semantic characteristics of trajectories. Works such as LCSS, EDR and MD-DTW are very rigid and limited to the order of the trajectory points, and two trajectories are considered similar if they match on all dimensions. On the other hand, works such as MSM are too flexible, considering two trajectories as similar if they match in any dimension. In this work, we define the concept of multiple-aspect trajectory, proposing the use of several attributes regarding different aspects related to movement. We propose MUITAS, a novel similarity measure for multiple-aspect trajectory similarity analysis, which overcomes the described limitations of previous works. MUITAS is evaluated on a toy example and over a real dataset of user check-ins on a social network containing different aspects related to movement. The results show that MUITAS is more accurate than existing similarity measures for analyzing multiple-aspect trajectories, in addition to allowing the analysis of trajectories in ways not explored before. pt_BR
dc.format.extent 67 pt_BR
dc.language.iso en pt_BR
dc.publisher Florianópolis, SC. pt_BR
dc.rights Open Access
dc.subject trajectory similarity pt_BR
dc.subject multiple-aspect trajectory pt_BR
dc.subject location-based social network pt_BR
dc.title Multiple-Aspect Trajectory Similarity Measuring pt_BR
dc.type TCCgrad pt_BR


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