Enabling Spatial OLAP over Environmental and Farming Data with QB4SOLAP

This in-use paper utilizes several governmental data sets from Danish ministries as a non-trivial use case to create and publish spatial data cubes on the Semantic Web. The data collection includes livestock farming data(CHR), environmental data, central company registry data(CVR) and various geographical data, which builds up an interesting use case to query with analytical spatial queries (SOLAP). These SOLAP queries reveral new perspectives from the combined data sets as spatial data cube.


Governmental organizations and agencies have been making large amounts of spatial data available on the Semantic Web (SW). However, we still lack efficient techniques for analyzing such large amounts of data as we know them from relational database systems, e.g., multidimensional (MD) data warehouses and On-line Analytical Processing (OLAP). A basic prerequisite to enable such advanced analytics is a well-defined schema, which can be defined using the QB4SOLAP vocabulary that provides sufficient context for spatial OLAP (SOLAP). In this paper, we address the challenging problem of MD querying with SOLAP operations on the SW by applying QB4SOLAP to a non-trivial spatial use case based on real-world open governmental data sets across various spatial domains.We describe the process of combining, interpreting, and publishing disparate spatial data sets as a spatial data cube on the SW and show how to query it with SOLAP operators.

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

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