Integrating continuous spatial data into SOLAP systems is a new research challenge. Moreover, representation of field data at different scales or resolutions is often mandatory for an effective analysis. Thus, in this paper, we propose a logical model to integrate spatial dimensions representing incomplete field data at different resolutions in a classical SOLAP architecture.

Résumé. L'intégration des données spatiales dans les modèles multidimensionnels conduit au concept d'OLAP Spatial (SOLAP). Les modèles SOLAP existants exploitent les données spatiales vectorielles. Peu de travaux intègrent les données spatiales continues (champs continus) en dimension et en mesure. Dans ce papier, nous proposons un modèle multidimensionnel qui utilise les champs continus comme ...

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...

The proliferation of spatial data and the publication of multidimensional (MD) data on the Semantic Web (SW) has led to new opportunities for On-Line Analytical Processing (SOLAP) over spatial data using SPARQL. However, formulating such queries results in verbose statements and can easily become very difficult for inexperienced users. Hence, we have developed GeoSemOLAP to enable users without...

It is well known that transactional and analysis systems each require a different database structure. In general, the database structure of transactional systems is optimized for consistency and efficient updates while the database structure of analysis systems is optimized for complex query performance. Non-spatial data are reorganized in data warehouses in order to support analysis and decisi...

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...

Spatial Data Warehouses (SDWs) and Spatial On-Line Analytical Processing (SOLAP) systems are new technologies for the integration and the analysis of huge volume of data with spatial reference. Spatial vagueness is often neglected in these types of systems and the data and analysis results are considered reliable. In a previous work, the authors provided a new design method for SOLAP datacubes ...

This paper presents a new category of decision-support tools that builds on today's Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP) technologies to facilitate Geographic Knowledge Discovery (GKD). This new category, named Spatial OLAP (SOLAP), has been an R&D topic for about 5 years in a few university labs and is now being implemented by early adopters in differen...

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...

To exploit the full potential of the spatial and temporal dimensions of a data warehouse, new tools are needed. It has been shown that OLAP possesses a certain potential to support spatio-temporal analysis. However, without a spatial interface for viewing and manipulating the geometric component of the spatial data, the analysis may be incomplete. A new category of OLAP tools, SOLAP (Spatial OL...