Geographic Information Systems (GIS) are systems designed to store, manipulate, analyse, and present geographical data. On the other hand, OLAP (On-Line Analytical Processing) are systems for efficiently querying multidimensional databases containing large amounts of data, usually called data warehouses. The integration of GIS and OLAP systems, called SOLAP, is essential to develop complex Business Intelligence (BI) applications that can support analysis over data stored in different kinds of media (like databases, data warehouses, maps).

There exist many proposals to facilitate the integration of GIS, OLAP, and multidimensional information. Even though this integration could be possible with existing technologies, it requires expensive ad-hoc solutions that include a significant amount of complex coding and are hardly portable. These solutions require data exchange between GIS and OLAP applications to be performed, which is complex and inefficient. Therefore, a general framework for BI that can integrate OLAP data and spatio-temporal data of different kinds (alphanumerical, vector, raster, temporal, multidimensional) is needed. To make things more difficult, in real-world applications spatial objects may be added, removed, split, merged, or their shape may change. Information in raster format (e.g., satellite images) is also updated frequently.

This complex scenario requires tools that provide an abstraction layer able to hide all this heterogeneity and technical problems from the user. The aim of this topic is investigate and propose these kinds of tools, together with a solid theoretical foundation for them.

Main Advisor at Aalborg Universitet (AAU)
Co-advisor at Université Libre de Bruxelles (ULB)