A geospatial source selector for federated GeoSPARQL querying

TL;DR

This paper introduces a geospatial source selection method for federated GeoSPARQL queries that uses spatial summaries to improve query efficiency by reducing unnecessary data source testing.

Contribution

It proposes a novel source selection technique based on bounding polygons to optimize federated GeoSPARQL query processing, with evaluation in an agroenvironmental use case.

Findings

More complex summaries increase source selection time but improve exclusion accuracy.

Effective source selection reduces overall query planning and execution times.

The method significantly enhances federated GeoSPARQL query efficiency in practical scenarios.

Abstract

Background: Geospatial linked data brings into the scope of the Semantic Web and its technologies, a wealth of datasets that combine semantically-rich descriptions of resources with their geo-location. There are, however, various Semantic Web technologies where technical work is needed in order to achieve the full integration of geospatial data, and federated query processing is one of these technologies. Methods: In this paper, we explore the idea of annotating data sources with a bounding polygon that summarizes the spatial extent of the resources in each data source, and of using such a summary as an (additional) source selection criterion in order to reduce the set of sources that will be tested as potentially holding relevant data. We present our source selection method, and we discuss its correctness and implementation. Results: We evaluate the proposed source selection using three different types of summaries with different degrees of accuracy, against not using geospatial summaries. We use datasets and queries from a practical use case that combines crop-type data with water availability data for food security. The experimental results suggest that more complex summaries lead to slower source selection times, but also to more precise exclusion of unneeded sources. Moreover, we observe the source selection runtime is (partially or fully) recovered by shorter planning and execution runtimes. As a result, the federated sources are not burdened by pointless querying from the federation engine. Conclusions: The evaluation draws on data and queries from the agroenvironmental domain and shows that our source selection method substantially improves the effectiveness of federated GeoSPARQL query processing.