Modeling and Querying Spatial
Data Warehouses on the Semantic Web

This research emphasizes on how to utilize spatial data warehousing on the Semantic Web by extending a multi-dimensional data cube model with spatial members. We present a spatially enhanced vocabulary as QB4SOLAP, published MD data as RDF with this vocabulary. Finally, as a proof-of-concept we implemented spatial and metric analysis on spatial members along with OLAP operations and re-defined them as spatial OLAP (SOLAP) operators.


The Semantic Web 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 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. Importance and plausibility 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. In this paper we propose to utilize the spatial dimension of the cube by adding spatial object type and topological relationships to the existing QB4OLAP vocabulary. Thus we can 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 meta-model as QB4SOLAP and apply on a use case scenario. Finally, we conclude the paper showing how these SOLAP queries can be expressed in SPARQL.

Authors: Nurefşan Gür, Katja Hose, Torben Bach Pedersen and Esteban Zimányi

QB4SOLAP Meta-Model (v1.1)

RDF Vocabulary

QB4SOLAP Versions

The versions of the QB4SOLAP vocabulary and the schemas can be found from the following links.

RDF Files

RDF files for the QB4SOLAP vocabulary, use case schema, and instances can be found in the following links.

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