Turning a collection of simple time-geography data into mobility knowledgeis a key issue in many research domains, such as social analysis and mobility investigation. Although collecting mobility data has become technologically feasible, turning these huge sets of data into mobility knowledge is still an open issue. Data warehousing is a well-established technique used for analysis of summarize...

In this paper, we study some problems linked to the integration of data in a spatio-temporal data warehouse. In many cases, the specifications of the data sets have evolved over time, especially when the observed period is large. Under those circumstances, data sources have temporal, spatial and semantic heterogeneity. In order to explore and analyse spatio-temporal data sets in a SOLAP (Spatia...

This paper proposes a novel, probabilistic data model and algebra that improves the modeling and querying of uncertain data in spatial OLAP (SOLAP) to support location-based services. Data warehouses that support location-based services need to combine complex hierarchies, such as road networks or transportation infrastructures, with static and dynamic content, e.g., speed limits and vehicle po...

Cartographic generalization has been a research topic for more than thirty years. In spite of recent advances, a complete automation of this process still faces fundamental problems. Furthermore, recent needs such as web mapping and Spatial On-Line Analytical Processing (SOLAP) require geospatial data that are generated on-the-fly at any scale. This requires processing times and a degree of aut...

Maritime shipping is a complex process because it requires continuous analysis of huge amounts information. Much this information has variable spatial and temporal properties. In addition, the must be effective to help in making decisions timely manner. Generally applied with Spatial On-Line Analytical Processing (SOLAP) basing on data warehouses SDW. Data warehousing by current technologies re...

Data warehouses and OLAP systems help to analyze complex multidimensional data and provide decision support. With the availability of large amounts of spatial data in recent years, several new models have been proposed to enable the integration of spatial data in data warehouses and to help analyze such data. This is often achieved by a combination of GIS and spatial analysis tools with OLAP an...

Spatial multidimensional databases (also called "spatial datacubes") are the cornerstone of the emerging Spatial On-Line Analytical Processing technology (SOLAP). They are aimed at supporting Geographic Knowledge Discovery (GKD) as well as certain types of spatial decision-making. Although these technologies seem promising at first glance, they may provide unreliable results if one does not con...

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...

Analytic and geographic decision support queries are characterized by performing spatial analysis on data aggregated at different levels of detail and are typically large and complex to be written from scratch (manually) by a non-specialist user. Considering that decision support tools are in evidence and many databases (DB) have information with some geographic reference, nonspecialist users o...

Spatial datacubes (also called "spatial multidimensional databases") are the cornerstone of the emerging Spatial On-Line Analytical Processing (SOLAP) technology. They are aimed at supporting Geographic Knowledge Discovery (GKD) as well as certain types of spatial decision-making. Although these technologies seem promising at first glance, they may provide unreliable results if one does not con...