Abstract
In the era of big data, a vast amount of geospatial data has become available originating from a large diversity of sources. In most cases, this data does not follow the linked data paradigm and the existing transformation tools have been proved ineffective due to the large volume and velocity of geospatial data. This is because none of the existing tools can utilize effectively the processing power of clusters of computers. We present the system GeoTriples-Spark which is able to massively transform big geospatial data into RDF graphs using Apache Spark. We evaluate GeoTriple-Spark’s performance and scalability in standalone and distributed environments and show that it exhibits superior performance and scalability when compared to all of its competitors.
The present work was funded by the European Union’s Horizon 2020 research and innovation project under grant agreement No 825258.
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Mandilaras, G., Koubarakis, M. (2021). Scalable Transformation of Big Geospatial Data into Linked Data. In: Hotho, A., et al. The Semantic Web – ISWC 2021. ISWC 2021. Lecture Notes in Computer Science(), vol 12922. Springer, Cham. https://doi.org/10.1007/978-3-030-88361-4_28
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