Including spatial relations and scales within sequential pattern extraction
Identifieur interne : 001906 ( Main/Merge ); précédent : 001905; suivant : 001907Including spatial relations and scales within sequential pattern extraction
Auteurs : Mickaël Fabrègue [France] ; Agnès Braud [France] ; Sandra Bringay [France] ; Florence Le Ber [France] ; Maguelonne Teisseire [France]Source :
English descriptors
Abstract
Georeferenced databases contain a huge volume of temporal and spatial data. They are notably used in environmental analysis. Sev- eral works address the problem of mining those data, but none are able to take into account the richness of the data and especially their spatial and temporal dimensions. In this paper, we focus on the extraction of a new kind of spatio-temporal pattern, considering the relationship be- tween spatial objects and geographical scales. We propose an algorithm, STR PrefixGrowth, which can be applied to a huge amount of data. The proposed method is evaluated on hydrological data collected on the Saône watershed during the last 19 years. Our experiments emphasize the contribution of our approach toward the existing methods.
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500 rue Jean-François Breton - 34093 Montpellier Cedex 5</addrLine><country key="FR"></country></address><ref type="url">http://tetis.teledetection.fr/</ref></desc><listRelation><relation name="UMR91" active="#struct-11574" type="direct"></relation><relation active="#struct-148117" type="direct"></relation><relation active="#struct-302049" type="direct"></relation></listRelation><tutelles><tutelle name="UMR91" active="#struct-11574" type="direct"><org type="institution" xml:id="struct-11574" status="VALID"><orgName>Centre de Coopération Internationale en Recherche Agronomique pour le Développement</orgName><orgName type="acronym">CIRAD</orgName><desc><address><addrLine>42, rue Scheffer 75116 Paris</addrLine><country key="FR"></country></address><ref type="url">http://www.cirad.fr</ref></desc></org></tutelle><tutelle active="#struct-148117" type="direct"><org type="institution" xml:id="struct-148117" status="VALID"><orgName>AgroParisTech</orgName><orgName type="acronym">AgroParisTech</orgName><desc><address><addrLine>Institut des sciences et industries du vivant et de l'environnement - AgroParisTech - 16 rue Claude Bernard F-75231 Paris Cedex 05</addrLine><country key="FR"></country></address><ref type="url">http://www.agroparistech.fr/</ref></desc></org></tutelle><tutelle active="#struct-302049" type="direct"><org type="institution" xml:id="struct-302049" status="VALID"><orgName>Irstea</orgName><desc><address><country key="FR"></country></address><ref type="url">http://www.irstea.fr</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Aquatic ecosystem</term><term>Data Mining</term><term>Sequential patterns</term><term>Spatio-temporal</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Georeferenced databases contain a huge volume of temporal and spatial data. They are notably used in environmental analysis. Sev- eral works address the problem of mining those data, but none are able to take into account the richness of the data and especially their spatial and temporal dimensions. In this paper, we focus on the extraction of a new kind of spatio-temporal pattern, considering the relationship be- tween spatial objects and geographical scales. We propose an algorithm, STR PrefixGrowth, which can be applied to a huge amount of data. The proposed method is evaluated on hydrological data collected on the Saône watershed during the last 19 years. Our experiments emphasize the contribution of our approach toward the existing methods.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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