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Panda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica, vol 21 n° 2 (April - June 2017)  

Public [article]  inGeoinformatica > vol 21 n° 2 (April - June 2017) . - pp 175 - 208 Titre : Panda∗: A generic and scalable framework for predictive spatio-temporal queries Type de document : Article/Communication Auteurs : Abdeltawab M. Hendawi, Auteur ; Mohamed Ali, Auteur ; Mohamed F. Mokbel, Auteur Année de publication : 2017 Article en page(s) : pp 175 - 208 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] environnement de développement [Termes IGN] espace euclidien [Termes IGN] gestion de trafic [Termes IGN] objet mobile [Termes IGN] plus proche voisin, algorithme du [Termes IGN] prédiction [Termes IGN] requête spatiotemporelle Résumé : (Auteur) Predictive spatio-temporal queries are crucial in many applications. Traffic management is an example application, where predictive spatial queries are issued to anticipate jammed areas in advance. Also, location-aware advertising is another example application that targets customers expected to be in the vicinity of a shopping mall in the near future. In this paper, we introduce Panda∗, a generic framework for supporting spatial predictive queries over moving objects in Euclidean spaces. Panda∗ distinguishes itself from previous work in spatial predictive query processing by the following features: (1) Panda∗ is generic in terms of supporting commonly-used types of queries, (e.g., predictive range, KNN, aggregate queries) over stationary points of interests as well as moving objects. (2) Panda∗ employees a prediction function that provides accurate prediction even under the absence or the scarcity of the objects’ historical trajectories. (3) Panda∗ is customizable in the sense that it isolates the prediction calculation from query processing. Hence, it enables the injection and integration of user defined prediction functions within its query processing framework. (4) Panda∗ deals with uncertainties and variabilities in the expected travel time from source to destination in response to incomplete information and/or dynamic changes in the underlying Euclidean space. (5) Panda∗ provides a controllable parameter that trades low latency responses for computational resources. Experimental analysis proves the scalability of Panda∗ in evaluating a massive volume of predictive queries over large numbers of moving objects. Numéro de notice : A2017-068 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0284-8 En ligne : http://dx.doi.org/10.1007/s10707-016-0284-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84295 [article]

Design principles of a stream-based framework for mobility analysis / Loic Salmon in Geoinformatica, vol 21 n° 2 (April - June 2017)  

Public [article]  inGeoinformatica > vol 21 n° 2 (April - June 2017) . - pp 237 - 261 Titre : Design principles of a stream-based framework for mobility analysis Type de document : Article/Communication Auteurs : Loic Salmon, Auteur ; Cyril Ray, Auteur Année de publication : 2017 Article en page(s) : pp 237 - 261 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatiale [Termes IGN] approche holistique [Termes IGN] cartographie à la volée [Termes IGN] flux de données [Termes IGN] gestion de trafic [Termes IGN] navigation maritime [Termes IGN] objet mobile [Termes IGN] positionnement cinématique en temps réel [Termes IGN] système de gestion de flux de données [Termes IGN] temps réel [Termes IGN] traitement interactif [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Trajectory analysis is of crucial importance in several fields as social analysis, zoology, climatology or traffic monitoring. Over the last decade, the number of mobile systems and devices recording their positions has grown significantly generating a deluge of spatial and temporal data to analyze. This increasing volume of data raises numerous issues in terms of storage, processing and extraction of information. Previous works considering movement analysis have been mainly oriented towards either archived data processing and mining or continuous handling of incoming streams. The research developed in this paper introduces the design principles of a holistic approach combining real-time processing and archived data analysis to process mobility data “on the fly”. This solution aims to provide better results comparing to both purely offline and online approaches. This research considers distributed data and processing to be more efficient. The design principles are applied to maritime traffic analysis and a few representative examples are introduced to demonstrate the relevance of our approach. Numéro de notice : A2017-070 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0256-z En ligne : http://dx.doi.org/10.1007/s10707-016-0256-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84297 [article]

Distributed processing of big mobility data as spatio-temporal data streams / Zdravko Galić in Geoinformatica, vol 21 n° 2 (April - June 2017)  

Public [article]  inGeoinformatica > vol 21 n° 2 (April - June 2017) . - pp 263 - 291 Titre : Distributed processing of big mobility data as spatio-temporal data streams Type de document : Article/Communication Auteurs : Zdravko Galić, Auteur ; Emir Mešković, Auteur ; Dario Osmanović, Auteur Année de publication : 2017 Article en page(s) : pp 263 - 291 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] données massives [Termes IGN] données spatiotemporelles [Termes IGN] environnement de développement [Termes IGN] flux de données [Termes IGN] mobilité territoriale [Termes IGN] mobilité urbaine [Termes IGN] objet mobile [Termes IGN] prototype [Termes IGN] temps réel [Termes IGN] traitement réparti Résumé : (Auteur) Recent rapid development of wireless communication, mobile computing, global navigation satellite systems (GNSS), and spatially enabled sensors are leading to an exponential growth of available mobility data produced continuously at high speed. Due to these advancements, a new class of monitoring applications has come to the focus, including real-time intelligent transportation systems, traffic monitoring and mobile objects tracking. These new information flow processing (IFP) application domains need to process huge volume of mobility data arriving in the form of continuous data streams from mobile objects. IFP applications are pushing traditional database technologies beyond their limits due to their massively increasing data volumes and demands for real-time processing. Mobility data, i.e. real-time, transient, time-varying sequences of spatio-temporal data items, generated by embedded positioning sensors demonstrates at least two Big Data core features: volume and velocity. Existing distributed data stream management systems (DSMS), real-time computing systems (RTCS) and their processing models are dominantly based on relational paradigm and continuous operator model. Thus, they have rudimentary spatio-temporal capabilities, provide expensive fault recovery requiring either hot replication or long recovery times, and do not handle faults and slow nodes. The framework proposed in this paper is a cornerstone towards efficient real-time managing and monitoring of mobile objects through distributed spatio-temporal streams processing on large clusters. A prototype implementation is rooted in a new stream processing model that overcomes the challenges of current distributed stream processing models and enable seamless integration with batch and interactive processing like MapReduce. Numéro de notice : A2017-069 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0264-z En ligne : http://dx.doi.org/10.1007/s10707-016-0264-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84300 [article]