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Efficient maximal reverse skyline query processing / Farnoush Banaei-Kashani in Geoinformatica, vol 21 n° 3 (July - September 2017)  

Public [article]  inGeoinformatica > vol 21 n° 3 (July - September 2017) . - pp 549 - 572 Titre : Efficient maximal reverse skyline query processing Type de document : Article/Communication Auteurs : Farnoush Banaei-Kashani, Auteur ; Parisa Ghaemi, Auteur ; Bahman Movaqar, Auteur ; Seyed Jalal Kazemitabar, Auteur Année de publication : 2017 Article en page(s) : pp 549 - 572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] analyse multicritère [Termes IGN] espace métrique [Termes IGN] jeu de données localisées [Termes IGN] opérateur skyline [Termes IGN] requête (informatique) [Termes IGN] requête spatiale inverse Résumé : (Auteur) Given a set S of sites and a set O of objects in a metric space, the Optimal Location (OL) problem is about computing a location in the space where introducing a new site (e.g., a retail store) maximizes the number of the objects (e.g., customers) that would choose the new site as their “preferred” site among all sites. However, the existing solutions for the optimal location problem assume that there is only one criterion to determine the preferred site for each object, whereas with numerous real-world applications multiple criteria are used as preference measures. For example, while a single criterion solution might consider the metric distance between the customers and the retail store as the preference measure, a multi-criteria solution might consider the annual membership cost as well as the distance to the retail store to find an optimal location. In this paper, for the first time we develop an efficient and exact solution for the so-called Multi-Criteria Optimal Location (MCOL) problem that can scale with large datasets. Toward that end, first we formalize the MCOL problem as maximal reverse skyline query (MaxRSKY). Given a set of sites and a set of objects in a d-dimensional space, MaxRSKY query returns a location in the space where if a new site s is introduced, the size of the (bichromatic) reverse skyline set of s is maximal. To the best of our knowledge, this paper is the first to define and study MaxRSKY query. Accordingly, we propose a filter-based solution, termed EF-MaxRSKY, that effectively prunes the search space for efficient identification of the optimal location. Our extensive empirical analysis with both real and synthetic datasets show that EF-MaxRSKY is invariably efficient in computing answers for MaxRSKY queries with large datasets containing thousands of sites and objects. Numéro de notice : A2017-381 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-017-0302-5 En ligne : https://doi.org/10.1007/s10707-017-0302-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85812 [article]

A comparative study of two approaches for supporting optimal network location queries / Parisa Ghaemi in Geoinformatica, vol 18 n° 2 (April 2014)  

Public [article]  inGeoinformatica > vol 18 n° 2 (April 2014) . - pp 229 - 251 Titre : A comparative study of two approaches for supporting optimal network location queries Type de document : Article/Communication Auteurs : Parisa Ghaemi, Auteur ; Kaveh Shahabi, Auteur ; John P. Wilson, Auteur ; Farnoush Banaei-Kashani, Auteur Année de publication : 2014 Article en page(s) : pp 229 - 251 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse comparative [Termes IGN] base de données spatiotemporelles [Termes IGN] distance [Termes IGN] étude d'implantation [Termes IGN] géomercatique [Termes IGN] optimisation (mathématiques) [Termes IGN] requête spatiale [Termes IGN] réseau routier Résumé : (Auteur) Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald’s) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L1 and L2/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this article, we focus on optimal network location (ONL) queries, i.e., optimal location queries in which objects and sites reside on a spatial network. We introduce two complementary approaches, namely EONL (short for Expansion-based ONL) and BONL (short for Bound-based ONL), which enable efficient computation of ONL queries with datasets of uniform and skewed distributions, respectively. Moreover, with an extensive experimental study, we verify and compare the efficiency of our proposed approaches with real world datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries. Numéro de notice : A2014-225 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-013-0179-x Date de publication en ligne : 28/04/2013 En ligne : https://doi.org/10.1007/s10707-013-0179-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33128 [article]

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