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Unveiling movement uncertainty for robust trajectory similarity analysis / Andre Salvaro Furtado in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 1-2 (January - February 2018) . - pp 140 - 168 Titre : Unveiling movement uncertainty for robust trajectory similarity analysis Type de document : Article/Communication Auteurs : Andre Salvaro Furtado, Auteur ; Luis Otavio Alvares, Auteur ; Nikos Pelekis, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 140 - 168 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] distance [Termes IGN] données localisées [Termes IGN] incertitude des données [Termes IGN] mesure de similitude multidimensionnelle [Termes IGN] méthode robuste [Termes IGN] trace GPS [Termes IGN] trajet (mobilité) Résumé : (Auteur) Trajectory data analysis and mining require distance and similarity measures, and the quality of their results is directly related to those measures. Several similarity measures originally proposed for time-series were adapted to work with trajectory data, but these approaches were developed for well-behaved data that usually do not have the uncertainty and heterogeneity introduced by the sampling process to obtain trajectories. More recently, similarity measures were proposed specifically for trajectory data, but they rely on simplistic movement uncertainty representations, such as linear interpolation. In this article, we propose a new distance function, and a new similarity measure that uses an elliptical representation of trajectories, being more robust to the movement uncertainty caused by the sampling rate and the heterogeneity of this kind of data. Experiments using real data show that our proposal is more accurate and robust than related work. Numéro de notice : A2018-023 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1372763 En ligne : https://doi.org/10.1080/13658816.2017.1372763 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89175 [article]

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Multidimensional Similarity Measuring for Semantic Trajectories / Andre Salvaro Furtado in Transactions in GIS, vol 20 n° 2 (April 2016)  

Public [article]  inTransactions in GIS > vol 20 n° 2 (April 2016) . - pp 280 – 298 Titre : Multidimensional Similarity Measuring for Semantic Trajectories Type de document : Article/Communication Auteurs : Andre Salvaro Furtado, Auteur ; Despina Kopanaki, Auteur ; Luis Otavio Alvares, Auteur ; Vania Bogorny, Auteur Année de publication : 2016 Article en page(s) : pp 280 – 298 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] information sémantique [Termes IGN] itinéraire piétionnier [Termes IGN] mesure de similitude multidimensionnelle [Termes IGN] similitude sémantique [Termes IGN] trajet (mobilité) Résumé : (auteur) Most existing approaches aiming at measuring trajectory similarity are focused on two-dimensional sequences of points, called raw trajectories. However, recent proposals have used background geographic information and social media data to enrich these trajectories with a semantic dimension, giving rise to the concept of semantic trajectories. Only a few works have proposed similarity measures for semantic trajectories or multidimensional sequences, having limitations such as predefined weight of the dimensions, sensitivity to noise, tolerance for gaps with different sizes, and the prevalence of the worst dimension similarity. In this article we propose MSM, a novel similarity measure for multidimensional sequences that overcomes the aforementioned limitations by considering and weighting the similarity in all dimensions. MSM is evaluated through an extensive experimental study that, based on a seed trajectory, creates sets of semantic trajectories with controlled transformations to introduce different kinds and levels of dissimilarity. For each set, we compute the similarity between the seed and the transformed trajectories, using different measures. The results showed that MSM was more robust and efficient than related approaches in the domain of semantic trajectories. Numéro de notice : A2016-452 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12156 En ligne : http://dx.doi.org/10.1111/tgis.12156 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81363 [article]