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Auteur Abdeltawab M. Hendawi |
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Road network simplification for location-based services / Abdeltawab M. Hendawi in Geoinformatica, vol 24 n° 4 (October 2020)
[article]
Titre : Road network simplification for location-based services Type de document : Article/Communication Auteurs : Abdeltawab M. Hendawi, Auteur ; John A. Stankovic, Auteur ; Ayman Taha, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 801 - 826 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker
[Termes IGN] appariement de cartes
[Termes IGN] appariement de données localisées
[Termes IGN] appariement de graphes
[Termes IGN] carte routière
[Termes IGN] compression de données
[Termes IGN] modèle de Markov caché
[Termes IGN] réseau routier
[Termes IGN] service fondé sur la position
[Termes IGN] simplification de contour
[Termes IGN] stockage de données
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Road-network data compression or simplification reduces the size of the network to occupy less storage with the aim to fit small form-factor routing devices, mobile devices, or embedded systems. Simplification (a) reduces the storage cost of memory and disks, and (b) reduces the I/O and communication overhead. There are several road network compression techniques proposed in the literature. These techniques are evaluated by their compression ratios. However, none of these techniques takes into consideration the possibility that the generated compressed data can be used directly in Map-matching operation which is an essential component for all location-aware services. Map-matching matches a measured latitude and longitude of an object to an edge in the road network graph. In this paper, we propose a novel simplification technique, named COMA, that (1) significantly reduces the size of a given road network graph, (2) achieves high map-matching quality on the simplified graph, and (3) enables the generated compressed road network graph to be used directly in map-matching and location-based applications without a need to decompress it beforehand. COMA smartly deletes those nodes and edges that will not affect the graph connectivity nor causing much of ambiguity in the map-matching of objects’ location. COMA employs a controllable parameter; termed a conflict factor C, whereby location aware services can trade the compression gain with map-matching accuracy at varying granularity. We show that the time complexity of our COMA algorithm is O(|N|log|N|). Intensive experimental evaluation based on a real implementation and data demonstrates that COMA can achieve about a 75% compression-ratio while preserving high map-matching quality. Road Network, Simplification, Compression, Spatial, Location, Performance, Accuracy, Efficiency, Scalability. Numéro de notice : A2020-495 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00406-x Date de publication en ligne : 01/05/2020 En ligne : https://doi.org/10.1007/s10707-020-00406-x Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96115
in Geoinformatica > vol 24 n° 4 (October 2020) . - pp 801 - 826[article]Panda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica, vol 21 n° 2 (April - June 2017)
[article]
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 spatiotemporelleRésumé : (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
in Geoinformatica > vol 21 n° 2 (April - June 2017) . - pp 175 - 208[article]