The integration of Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP), denoted SOLAP, is aimed at exploring and analyzing spatial data. In real-world SOLAP applications, spatial and non-spatial data are subject to changes. In this paper we present a temporal query language for SOLAP, called TPiet-QL, supporting so-called discrete changes (for example, in land use or c...

XML and Web Services technologies have revolutionized the way data are exchanged on the Internet. Meanwhile, Spatial OLAP (SOLAP) tools have emerged to bridge the gap between the Business Intelligence and Geographic Information Systems domains. While Web Services specifications such as XML for Analysis enable the use of OLAP tools in Service Oriented Architecture (SOA) environments, no solution...

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Existing SOLAP models do not completely integrate the semantic component of geographic information (alphanumeric attributes and relationships) or the flexibility of spatial analysis into multidimensional analysis. In this chapter, the authors propose the GeoCube model and its associated operators...

D'importants efforts sont déployés depuis une quinzaine d'années pour mettre en place des systèmes d'aide à la décision sur le territoire. Ces systèmes reposent toutefois sur les systèmes d'information géographique (SIG) et les approches transactionnelles habituelles (OLTP) pour produire l'information géodécisionnelle, souvent avec des délais inacceptables, voire des coûts prohibitifs au point ...

Spatial data warehouses (SDW) and spatial OLAP (SOLAP) systems are well-known business intelligence technologies that aim to support a multidimensional and online analysis for a large volume of geo-referenced datasets. SOLAP systems are already used in the context of natural hazards for analyzing sensor data and experts’ measurements. Recently, new data gathering tools coined as volunteered geo...

Large volumes of geospatial data are being published on the Semantic Web (SW), yielding a need for advanced analysis of such data. However, existing SW technologies only support advanced analytical concepts such as multidimensional (MD) data warehouses and Online Analytical Processing (OLAP) over non-spatial SW data. To remedy this need, this paper presents the QB4SOLAP vocabulary, which suppor...

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, ve...

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GISbased Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, ven...

Large volumes of spatial data and multidimensional are being published on the Semantic Web, which has led to new opportunities for advanced analysis, such as Spatial Online Analytical Processing (SOLAP). The RDF Data Cube (QB) QB4OLAP vocabularies have been widely used annotating publishing statistical data. Although sets might information, coordinates, lack semantics concepts in QB prevents us...

Résumé. Les entrepôts de données spatiales et les systèmes SOLAP permettent l’analyse en ligne de gros volumes de données géo référencées. Dans un contexte de gestion des risques naturels, ces systèmes ont été utilisés avec succès sur des données officielles dont la collecte suit un processus bien établi. Le développement récent d’outils collaboratifs pour la saisie de données géographiques, y ...