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HiPerMovelets: high-performance movelet extraction for trajectory classification / Tarlis Tortelli Portela in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 5 (May 2022) . - pp 1012 - 1036 Titre : HiPerMovelets: high-performance movelet extraction for trajectory classification Type de document : Article/Communication Auteurs : Tarlis Tortelli Portela, Auteur ; Jonata Tyska Carvalho, Auteur ; Vania Bogorny, Auteur Année de publication : 2022 Article en page(s) : pp 1012 - 1036 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification [Termes IGN] exploration de données géographiques [Termes IGN] jeu de données localisées [Termes IGN] trace numérique [Termes IGN] trajet (mobilité) Résumé : (auteur) In the last decade, trajectory classification has received significant attention. The vast amount of data generated on social media, the use of sensor networks, IOT devices and other Internet-enabled sources allowed the semantic enrichment of mobility data, making the classification task more challenging. Existing trajectory classification methods have mainly considered space, time and numerical data, ignoring the semantic dimensions. Only recently proposed methods as Movelets and MASTERMovelets can handle all types of dimensions. MASTERMovelets is the only method that automatically discovers the best dimension combination and subtrajectory size for trajectory classification. However, although it outperformed the state-of-the-art in terms of accuracy, MASTERMovelets is computationally expensive and results in a high dimensionality problem, which makes it unfeasible for most real trajectory datasets that contain a big volume of data. To overcome this problem and enable the application of the movelets approach on large datasets, in this paper we propose a new high-performance method for extracting movelets and classifying trajectories, called HiPerMovelets (High-performance Movelets). Experimental results show that HiPerMovelets is 10 times faster than MASTERMovelets, reduces the high-dimensionality problem, is more scalable, and presents a high classification accuracy in all evaluated datasets with both raw and semantic trajectories. Numéro de notice : A2022-332 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2021.2018593 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1080/13658816.2021.2018593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100608 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2022051	SL	Revue	Centre de documentation	Revues en salle	Disponible

SMSM: a similarity measure for trajectory stops and moves / Andre L. Lehmann in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1847 - 1872 Titre : SMSM: a similarity measure for trajectory stops and moves Type de document : Article/Communication Auteurs : Andre L. Lehmann, Auteur ; Luis Otavio Alvares, Auteur ; Vania Bogorny, Auteur Année de publication : 2019 Article en page(s) : pp 1847 - 1872 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] calcul d'itinéraire [Termes IGN] durée de trajet [Termes IGN] information sémantique [Termes IGN] mesure de similitude [Termes IGN] objet mobile [Termes IGN] relation sémantique [Termes IGN] taxi [Termes IGN] trajet (mobilité) Résumé : (auteur) For many years trajectory similarity research has focused on raw trajectories, considering only space and time information. With the trajectory semantic enrichment, emerged the need for similarity measures that support space, time, and semantics. Although some trajectory similarity measures deal with all these dimensions, they consider only stops, ignoring the moves. We claim that, for some applications, the movement between stops is as important as the stops, and they must be considered in the similarity analysis. In this article, we propose SMSM, a novel similarity measure for semantic trajectories that considers both stops and moves. We evaluate SMSM with three trajectory datasets: (i) a synthetic trajectory dataset generated with the Hermoupolis semantic trajectory generator, (ii) a real trajectory dataset from the CRAWDAD project, and (iii) the Geolife dataset. The results show that SMSM overcomes state-of-the-art measures developed either for raw or semantic trajectories. Numéro de notice : A2019-391 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1605074 Date de publication en ligne : 24/06/2019 En ligne : https://doi.org/10.1080/13658816.2019.1605074 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93486 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2019091	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2019092	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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]

CONSTAnT – A conceptual data model for semantic Trajectories of moving objects / Vania Bogorny in Transactions in GIS, vol 18 n° 1 (February 2014)  

Public [article]  inTransactions in GIS > vol 18 n° 1 (February 2014) . - pp 66 - 88 Titre : CONSTAnT – A conceptual data model for semantic Trajectories of moving objects Type de document : Article/Communication Auteurs : Vania Bogorny, Auteur ; Chiara Renso, Auteur ; Artur Ribeiro De Aquino, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 66 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] information sémantique [Termes IGN] itinéraire [Termes IGN] modèle conceptuel de données [Termes IGN] modèle sémantique de données [Termes IGN] objet mobile Résumé : (Auteur) Several works have been proposed in the last few years for raw trajectory data analysis, and some attempts have been made to define trajectories from a more semantic point of view. Semantic trajectory data analysis has received significant attention recently, but the formal definition of semantic trajectory, the set of aspects that should be considered to semantically enrich trajectories and a conceptual data model integrating these aspects from a broad sense is still missing. This article presents a semantic trajectory conceptual data model named CONSTAnT, which defines the most important aspects of semantic trajectories. We believe that this model will be the foundation for the design of semantic trajectory databases, where several aspects that make a trajectory “semantic” are taken into account. The proposed model includes the concepts of semantic subtrajectory, semantic points, geographical places, events, goals, environment and behavior, to create a general concept of semantic trajectory. The proposed model is the result of several years of work by the authors in an effort to add more semantics to raw trajectory data for real applications. Two application examples and different queries show the flexibility of the model for different domains. Numéro de notice : A2014-065 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12011 Date de publication en ligne : 05/02/2013 En ligne : https://doi.org/10.1111/tgis.12011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32970 [article]

Weka-STPM: a software architecture and prototype for semantic trajectory data mining and visualization / Vania Bogorny in Transactions in GIS, vol 15 n° 2 (April 2011)  

Public [article]  inTransactions in GIS > vol 15 n° 2 (April 2011) . - pp 227 - 248 Titre : Weka-STPM: a software architecture and prototype for semantic trajectory data mining and visualization Type de document : Article/Communication Auteurs : Vania Bogorny, Auteur ; H. Avancini, Auteur ; et al., Auteur Année de publication : 2011 Article en page(s) : pp 227 - 248 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] architecture logicielle [Termes IGN] exploration de données géographiques [Termes IGN] positionnement par GPS [Termes IGN] téléphonie mobile [Termes IGN] trace numérique [Termes IGN] trajectoire (véhicule non spatial) [Termes IGN] trajet (mobilité) [Termes IGN] visualisation de données [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Enormous quantities of trajectory data are collected from many sources, such as GPS devices and mobile phones, as sequences of spatio-temporal points. These data can be used in many application domains such as traffic management, urban planning, tourism, bird migration, and so on. Raw trajectory data, as generated by mobile devices have very little or no semantics, and in most applications a higher level of abstraction is needed to exploit these data for decision making. Although several different methods have been proposed so far for trajectory querying and mining, there are no software tools to help the end user with semantic trajectory data analysis. In this article we present a software architecture for semantic trajectory data mining as well as the first software prototype to enrich trajectory data with both semantic information and data mining. As a prototype we extend the Weka data mining toolkit with the module Weka-STPM, which is interoperable with databases constructed under OGC specifications. We tested Weka-STPM with real geographic databases, and trajectory data stored under the Postgresql/PostGIS DBMS. Numéro de notice : A2011-107 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2011.01246.x Date de publication en ligne : 04/04/2011 En ligne : https://doi.org/10.1111/j.1467-9671.2011.01246.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30887 [article]

Semantic-based pruning of redundant and uninteresting frequent geographic patterns / Vania Bogorny in Geoinformatica, vol 14 n° 2 (April 2010)  

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St-DMQL: a semantic trajectory data mining query language / Vania Bogorny in International journal of geographical information science IJGIS, vol 23 n°9-10 (september 2009)  

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Reducing uninteresting spatial association rules in geographic databases using background knowledge: a summary of results / Vania Bogorny in International journal of geographical information science IJGIS, vol 22 n° 4-5 (april 2008)  

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