Modeling and Querying Spatial Data Warehouses on the Semantic Web

Abstract

The Semantic Web (SW) has drawn the attention of data enthusiasts, and also inspired the exploitation and design of multidimensional data warehouses, in an unconventional way. Traditional data warehouses (DW) operate over static data. However multidimensional (MD) data modeling approach can be dynamically extended by defining both the schema and instances of MD data as RDF graphs. The importance and applicability of MD data warehouses over RDF is widely studied yet none of the works support a spatially enhanced MD model on the SW. Spatial support in DWs is a desirable feature for enhanced analysis, since adding encoded spatial information of the data allows to query with spatial functions. In this paper we propose to empower the spatial dimension of data warehouses by adding spatial data types and topological relationships to the existing QB4OLAP vocabulary, which already supports the representation of the constructs of the MD models in RDF. With QB4SOLAP, spatial constructs of the MD models can be also published in RDF, which allows to implement spatial and metric analysis on spatial members along with OLAP operations. In our contribution, we describe a set of spatial OLAP (SOLAP) operations, demonstrate a spatially extended metamodel as, QB4SOLAP, and apply it on a use case scenario. Finally, we show how these SOLAP queries can be expressed in SPARQL.