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Improving human mobility identification with trajectory augmentation / Fan Zhou in Geoinformatica, vol 25 n° 3 (July 2021)  

Public [article]  inGeoinformatica > vol 25 n° 3 (July 2021) . - pp 453 - 483 Titre : Improving human mobility identification with trajectory augmentation Type de document : Article/Communication Auteurs : Fan Zhou, Auteur ; Ruiyang Yin, Auteur ; Goce Trajcevski, Auteur ; Kunpeng Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 453 - 483 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] itinéraire [Termes IGN] mobilité humaine [Termes IGN] modèle numérique de déplacement [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau neuronal convolutif [Termes IGN] réseau neuronal récurrent [Termes IGN] utilisateur Résumé : (auteur) Many location-based social networks (LBSNs) applications such as customized Point-Of-Interest (POI) recommendation, preference-based trip planning, travel time estimation, etc., involve an important task of understanding human trajectory patterns. In particular, identifying and linking trajectories to users who generate them – a problem called Trajectory-User Linking (TUL) – has become a focus of many recent works. TUL is usually studied as a multi-class classification problem and has gained recent attention because: (1) the number of labels/classes (i.e., users) is way larger than the number of motion patterns among various trajectories; and (2) the location-based trajectory data, especially the check-ins – i.e., events of reporting a location at particular Point of Interest (POI) with known semantics – are often extremely sparse. Towards addressing these challenges, we introduce a Trajectory Generative Adversarial Network (TGAN) as an approach to enable learning users motion patterns and location distribution, and to eventually identify human mobility. TGAN consists of two jointly trained neural networks, playing a Minimax game to (iteratively) optimize both components. The first one is the generator, learning trajectory representation by a Recurrent Neural Network (RNN) based model, aiming at fitting the underlying trajectory distribution of a particular individual and generate synthetic trajectories with intrinsic invariance and global coherence. The second one is the discriminator – a Convolutional Neural Network (CNN) based model that discriminates the generated trajectory from the real ones and provides guidance to train the generator model. We demonstrate that the above two models can be well tuned together to improve the TUL performance, while achieving superior accuracy when compared to existing approaches. Numéro de notice : A2021-972 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-019-00378-7 Date de publication en ligne : 29/08/2019 En ligne : https://doi.org/10.1007/s10707-019-00378-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100390 [article]

Privacy-preserving detection of anomalous phenomena in crowdsourced environmental sensing using fine-grained weighted voting / Mihai Maruseac in Geoinformatica, vol 21 n° 4 (October - December 2017)  

Public [article]  inGeoinformatica > vol 21 n° 4 (October - December 2017) . - pp 733 - 762 Titre : Privacy-preserving detection of anomalous phenomena in crowdsourced environmental sensing using fine-grained weighted voting Type de document : Article/Communication Auteurs : Mihai Maruseac, Auteur ; Gabriel Ghinita, Auteur ; Goce Trajcevski, Auteur ; Peter Scheuermann, Auteur Année de publication : 2017 Article en page(s) : pp 733 - 762 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] modèle sémantique de données [Termes IGN] production participative [Termes IGN] protection civile [Termes IGN] protection de la vie privée [Termes IGN] source de données Résumé : (Auteur) This article addresses the problem of preserving privacy of individuals who participate in collaborative environmental sensing. We observe that in many applications of societal importance, one is interested in constructing a map of the spatial distribution of a given phenomenon (e.g., temperature, CO2 concentration, water polluting agents, etc.) and mobile users can contribute with providing measurements data. However, contributing data may leak sensitive private details, as an adversary could infer the presence of a person in a certain location at a given time. This, in turn, may reveal information about other contexts (e.g., health, lifestyle choices), and may even impact an individual’s physical safety. We introduce a technique for privacy-preserving detection of anomalous phenomena, where the privacy of the individuals participating in collaborative environmental sensing is protected according to the powerful semantic model of differential privacy. We propose a differentially-private index structure to address the specific needs of anomalous phenomenon detection and derive privacy preserving query strategies that judiciously allocate the privacy budget to maintain high data accuracy. In addition, we construct an analytical model to characterize the sensed value inaccuracy introduced by the differentially-private noise injection, derive error bounds, and perform a statistical analysis that allows us to improve accuracy by using custom weights for measurements in each cell of the index structure. Extensive experimental results show that the proposed approach achieves high precision in identifying anomalies, and incurs low computational overhead. Numéro de notice : A2017-602 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-017-0304-3 En ligne : https://doi.org/10.1007/s10707-017-0304-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86910 [article]

Towards fusing uncertain location data from heterogeneous sources / Bing Zhang in Geoinformatica, vol 20 n° 2 (April - June 2016)  

Public [article]  inGeoinformatica > vol 20 n° 2 (April - June 2016) . - pp 179 - 212 Titre : Towards fusing uncertain location data from heterogeneous sources Type de document : Article/Communication Auteurs : Bing Zhang, Auteur ; Goce Trajcevski, Auteur ; Liu Liu, Auteur Année de publication : 2016 Article en page(s) : pp 179 - 212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] appareil portable [Termes IGN] capteur passif [Termes IGN] données localisées [Termes IGN] données spatiotemporelles [Termes IGN] fusion de données [Termes IGN] incertitude de position [Termes IGN] objet mobile [Termes IGN] requête spatiotemporelle [Termes IGN] traitement de données localisées Résumé : (auteur) Properly incorporating location-uncertainties – which is, fully considering their impact when processing queries of interest – is a paramount in any application dealing with spatio-temporal data. Typically, the location-uncertainty is a consequence of the fact that objects cannot be tracked continuously and the inherent imprecision of localization devices. Although there is a large body of works tackling various aspects of efficient management of uncertainty in spatio-temporal data – the settings consider homogeneous localization devices, e.g., either a Global Positioning System (GPS), or different sensors (roadside, indoor, etc.). In this work, we take a first step towards combining the uncertain location data – i.e., fusing the uncertainty of moving objects location – obtained from both GPS devices and roadside sensors. We develop a formal model for capturing the whereabouts in time in this setting and propose the Fused Bead (FB) model, extending the bead model based solely on GPS locations. We also present algorithms for answering traditional spatio-temporal range queries, as well as a special variant pertaining to objects locations with respect to lanes on road segments – augmenting the conventional graph based road network with the width attribute. In addition, pruning techniques are proposed in order to expedite the query processing. We evaluated the benefits of the proposed approach on both real (Beijing taxi) and synthetic (generated from a customized trajectory generator) data. Our experiments demonstrate that the proposed method of fusing the uncertainties may eliminate up to 26 % of the false positives in the Beijing taxi data, and up to 40 % of the false positives in the larger synthetic dataset, when compared to using the traditional bead uncertainty models. Numéro de notice : A2016-371 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-015-0238-6 En ligne : http://dx.doi.org/10.1007/s10707-015-0238-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81133 [article]

Efficient maintenance of continuous queries for trajectories / H. Ding in Geoinformatica, vol 12 n° 3 (September - November 2008)  

Public [article]  inGeoinformatica > vol 12 n° 3 (September - November 2008) . - pp 255 - 288 Titre : Efficient maintenance of continuous queries for trajectories Type de document : Article/Communication Auteurs : H. Ding, Auteur ; Goce Trajcevski, Auteur ; Peter Scheuermann, Auteur Année de publication : 2008 Article en page(s) : pp 255 - 288 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données d'objets mobiles [Termes IGN] base de données localisées [Termes IGN] index spatial [Termes IGN] objet mobile [Termes IGN] Oracle 9I [Termes IGN] prise en compte du contexte [Termes IGN] prototype [Termes IGN] requête spatiale Résumé : (Auteur) We address the problem of optimizing the maintenance of continuous queries in Moving Objects Databases, when a set of pending continuous queries need to be reevaluated as a result of bulk updates to the trajectories of moving objects. Such bulk updates may happen when traffic abnormalities, e.g., accidents or road works, affect a subset of trajectories in the corresponding regions, throughout the duration of these abnormalities. The updates to the trajectories may in turn affect the correctness of the answer sets for the pending continuous queries in much larger geographic areas. We present a comprehensive set of techniques, both static and dynamic, for improving the performance of reevaluating the continuous queries in response to the bulk updates. The static techniques correspond to specifying the values for the various semantic dimensions of trigger execution. The dynamic techniques include an in-memory shared reevaluation algorithm, extending query indexing to queries described by trajectories and query reevaluation ordering based on space-filling curves. We have completely implemented our system prototype on top of an existing Object-Relational Database Management System, Oracle 9i, and conducted extensive experimental evaluations using realistic data sets to demonstrate the validity of our approach. Copyright Springer Numéro de notice : A2008-281 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-007-0029-9 En ligne : https://doi.org/10.1007/s10707-007-0029-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29274 [article]

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