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2021 | OriginalPaper | Buchkapitel

Scalable Transformation of Big Geospatial Data into Linked Data

verfasst von : George Mandilaras, Manolis Koubarakis

Erschienen in: The Semantic Web – ISWC 2021

Verlag: Springer International Publishing

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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.

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Titel: Scalable Transformation of Big Geospatial Data into Linked Data
verfasst von: George Mandilaras
Manolis Koubarakis
Verlag: Springer International Publishing
Buch: The Semantic Web – ISWC 2021
Print ISBN: 978-3-030-88360-7

Electronic ISBN: 978-3-030-88361-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-88361-4_28

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