The increasing availability of geo-referenced data has increased the need to enrich OLAP with spatial analysis, leading to the concept of Spatial OLAP (SOLAP). The conceptual modelling of spatial data cubes requires the definition of two kinds of metadata: (i) warehouse metadata that model data structures that maintain integrated data from multiple data sources and (ii) aggregation 1 1 In this paper, the term “aggregation” does not refer to UML aggregation associations. In the text of our article, “aggregation” is used in the sense of calculating a result (as in the field of databases) — the terms “aggregation level” and “aggregating relationship” refer to OLAP aggregations. metadata that specify how the warehoused data should be aggregated to meet the analysis goals of decision makers. In this paper we provide a review of existing conceptual spatial data cube models. We highlight some limits of these models concerning the aggregation model design, and their implementation in existing CASE tools and SOLAP architectures. Firstly, we propose a new UML (Unified Modeling Language) profile for modelling complex Spatial Data Warehouses and aggregations. Our profile is implemented in the MagicDraw CASE tool. Secondly, we propose a tool for the automatic implementation of conceptual spatial data cube models, designed using our profile, in a SOLAP architecture. In particular, our solution allows: (i) generating different logical representations of the SDW (Spatial Data Warehouse) model (star schema and snow-flake schema) and (ii) implementing complex SOLAP analysis indicators using MDX (MultiDimensional eXpressions language). 
Conceptual model for spatial data cubes: A UML profile and its automatic implementation