Centre de documentation
scientifique

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]

Generic and efficient framework for search trees on flash memory storage systems / Mohamed Sarwat in Geoinformatica, vol 17 n° 3 (July 2013)  

Public [article]  inGeoinformatica > vol 17 n° 3 (July 2013) . - pp 489 - 519 Titre : Generic and efficient framework for search trees on flash memory storage systems Type de document : Article/Communication Auteurs : Mohamed Sarwat, Auteur ; Mohamed F. Mokbel, Auteur ; Xun Zhou, Auteur ; Suman Nath, Auteur Année de publication : 2013 Article en page(s) : pp 489 - 519 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique [Termes IGN] arbre (mathématique) [Termes IGN] arbre-R [Termes IGN] indexation spatiale [Termes IGN] mémoire d'ordinateur [Termes IGN] mémoire flash Résumé : (Auteur) Tree index structures are crucial components in data management systems. Existing tree index structure are designed with the implicit assumption that the underlying external memory storage is the conventional magnetic hard disk drives. This assumption is going to be invalid soon, as flash memory storage is increasingly adopted as the main storage media in mobile devices, digital cameras, embedded sensors, and notebooks. Though it is direct and simple to port existing tree index structures on the flash memory storage, that direct approach does not consider the unique characteristics of flash memory, i.e., slow write operations, and erase-before-update property, which would result in a sub optimal performance. In this paper, we introduce FAST (i.e., Flash-Aware Search Trees) as a generic framework for flash-aware tree index structures. FAST distinguishes itself from all previous attempts of flash memory indexing in two aspects: (1) FAST is a generic framework that can be applied to a wide class of data partitioning tree structures including R-tree and its variants, and (2) FAST achieves both efficiency and durability of read and write flash operations through memory flushing and crash recovery techniques. Extensive experimental results, based on an actual implementation of FAST inside the GiST index structure in PostgreSQL, show that FAST achieves better performance than its competitors. Numéro de notice : A2013-381 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article DOI : 10.1007/s10707-012-0164-9 Date de publication en ligne : 30/08/2012 En ligne : https://doi.org/10.1007/s10707-012-0164-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32519 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-2013031	RAB	Revue	Centre de documentation	En réserve L003	Disponible