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| |betreuer=Saeed Taghizadeh | | |betreuer=Saeed Taghizadeh |
| |termin=Institutsseminar/2019-10-11 | | |termin=Institutsseminar/2019-10-11 |
| |kurzfassung=With the rapid increase of the number of GPS-enabled devices the amount of accumulated trajectory data is ever increasing. An important task in analyzing trajectory data is investigating which trajectories are similar to each other. Traditional models for trajectory similarity computation are based on dynamic programming. However, they suffer from scalability issues as well as susceptibility to noisy data. | | |kurzfassung=TBA |
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| A novel deep learning model for trajectory similarity computation (t2vec) emerged in 2018 and solved these two issues. However, we have no intuitive understanding what the similarity values obtained by t2vec are based on. In order to understand which applications are best suitable for t2vec, we need to analyze the similarity semantics which are captured by the deep learning model.
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| In this thesis we investigate how the parameters of the deep learning model influence the probability distributions of the similarity values. We already have an intuitive understanding of the traditional dynamic programming models. Therefore, we transfer this intuition onto t2vec by systematically comparing it to the traditional models. In the end we recommend suitable applications for t2vec, based on the results that we have gathered from our experiments.
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