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Semantic-based pruning of redundant and uninteresting frequent geographic patterns / Vania Bogorny in Geoinformatica, vol 14 n° 2 (April 2010)  

Public [article]  inGeoinformatica > vol 14 n° 2 (April 2010) . - pp 201 - 220 Titre : Semantic-based pruning of redundant and uninteresting frequent geographic patterns Type de document : Article/Communication Auteurs : Vania Bogorny, Auteur ; J. Valiati, Auteur ; L. Alvares, Auteur Année de publication : 2010 Article en page(s) : pp 201 - 220 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données localisées [Termes IGN] exploration de données géographiques [Termes IGN] modèle sémantique de données [Termes IGN] regroupement de données [Termes IGN] sémantique Résumé : (Auteur) In geographic association rule mining many patterns are either redundant or contain well known geographic domain associations explicitly represented in knowledge resources such as geographic database schemas and geo-ontologies. Existing spatial association rule mining algorithms are Apriori-like, and therefore generate a large amount of redundant patterns. For non-spatial data, the closed frequent pattern mining technique has been introduced to remove redundant patterns. This approach, however, does not warrant the elimination of both redundant and well known geographic dependences when mining geographic databases. This paper presents a novel method for pruning both redundant and well known geographic dependences, by pushing semantics into the pattern mining task. Experiments with real geographic databases have demonstrated a significant reduction of the total amount of patterns and the efficiency of the method. Copyright Springer Numéro de notice : A2010-065 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-009-0082-7 Date de publication en ligne : 06/05/2009 En ligne : https://doi.org/10.1007/s10707-009-0082-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30261 [article]

Exemplaires(1)

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057-2010021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

St-DMQL: a semantic trajectory data mining query language / Vania Bogorny in International journal of geographical information science IJGIS, vol 23 n°9-10 (september 2009)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 23 n°9-10 (september 2009) . - pp 1245 - 1276 Titre : St-DMQL: a semantic trajectory data mining query language Type de document : Article/Communication Auteurs : Vania Bogorny, Auteur ; Bart Kuijpers, Auteur ; L. Alvares, Auteur Année de publication : 2009 Article en page(s) : pp 1245 - 1276 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques [Termes IGN] attribut sémantique [Termes IGN] découverte de connaissances [Termes IGN] données localisées [Termes IGN] exploration de données géographiques [Termes IGN] langage de programmation [Termes IGN] langage de requête [Termes IGN] navigation [Termes IGN] objet mobile [Termes IGN] reconstruction d'itinéraire ou de trajectoire [Termes IGN] requête spatiotemporelle Résumé : (Auteur) Mobile devices are becoming very popular in recent years, and large amounts of trajectory data are generated by these devices. Trajectories left behind cars, humans, birds or other objects are a new kind of data which can be very useful in the decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user's point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery. Copyright Taylor & Francis Numéro de notice : A2009-388 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810802231449 En ligne : https://doi.org/10.1080/13658810802231449 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30018 [article]

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079-09061	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Reducing uninteresting spatial association rules in geographic databases using background knowledge: a summary of results / Vania Bogorny in International journal of geographical information science IJGIS, vol 22 n° 4-5 (april 2008)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 22 n° 4-5 (april 2008) . - pp 361 - 386 Titre : Reducing uninteresting spatial association rules in geographic databases using background knowledge: a summary of results Type de document : Article/Communication Auteurs : Vania Bogorny, Auteur ; Bart Kuijpers, Auteur ; L. Alvares, Auteur Année de publication : 2008 Article en page(s) : pp 361 - 386 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données localisées [Termes IGN] découverte de connaissances [Termes IGN] exploration de données géographiques [Termes IGN] règle d'association Résumé : (Auteur) Many association rule-mining algorithms have been proposed in the last few years. Their main drawback is the huge amount of generated patterns. In spatial association rule mining, besides the large amount of rules, many are well-known geographic domain associations explicitly represented in geographic database schemas. Existing algorithms have only considered the data, while the schema has not been considered. The result is that also the associations explicitly represented in geographic database schemas are extracted by association rule-mining algorithms. With the aim to reduce the number of well-known patterns and association rules, this paper presents a summary of results of a novel approach to extract patterns from geographic databases. A two step-pruning method is presented to avoid the generation of association rules that are previously known to be uninteresting. Experiments with real geographic databases show a considerable time reduction in both geographic data pre-processing and spatial association rule mining, with a very significant reduction in the total number of rules. Copyright Taylor & Francis Numéro de notice : A2008-144 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810701412991 En ligne : https://doi.org/10.1080/13658810701412991 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29139 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-08031	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-08032	RAB	Revue	Centre de documentation	En réserve L003	Disponible