A comprehensive overview of RDF for spatial and spatiotemporal data management

Fu, Zhang; Qingzhe, Lü; Du, Zhijiang; Chen, Xu; Cao, Chunhong

doi:10.1017/s0269888921000084

Cited by 2 publications

(1 citation statement)

References 72 publications

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“…In addition, topological relations among geospatial features can be determined and presented as links. For example, some efforts employ the topological predicate, "spatial:EQ", to integrate the NUTS and GADM datasets [33,34]. Ma [35] describes conversion from vector objects and raster data to RDF.…”

Section: Geospatial Links In Lodmentioning

confidence: 99%

Discovering Links between Geospatial Data Sources in the Web of Data: The Open Geospatial Engine Approach

He,

Liu

2024

IJGI

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The Web of Data has been fueled significantly by geospatial data over the last few years. In the current link discovery frameworks, there is still a lack of robust support for finding geospatial-aware links between geospatial data sources in the Web of Data. They are also limited in efficient association capabilities for large-scale datasets. This paper extends the data integration capability based on the spatial metrics in the open geospatial engine OGE. These metrics include topological relationships and spatial matching between geospatial entities within multiple geospatial data sources. Thus, the tool can be employed by data publishers to set geospatial-aware links to facilitate geospatial data and knowledge discovery in the Web of Data. Several geospatial data sources are used to demonstrate the usability and effectiveness of the approach and tool implementation.

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Section: Geospatial Links In Lodmentioning

confidence: 99%

Discovering Links between Geospatial Data Sources in the Web of Data: The Open Geospatial Engine Approach

He,

Liu

2024

IJGI

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MuSe: a multi-level storage scheme for big RDF data using MapReduce

2021

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Resource Description Framework (RDF) model owing to its flexible structure is increasingly being used to represent Linked data. The rise in amount of Linked data and Knowledge graphs has resulted in an increase in the volume of RDF data. RDF is used to model metadata especially for social media domains where the data is linked. With the plethora of RDF data sources available on the Web, scalable RDF data management becomes a tedious task. In this paper, we present MuSe—an efficient distributed RDF storage scheme for storing and querying RDF data with Hadoop MapReduce. In MuSe, the Big RDF data is stored at two levels for answering the common triple patterns in SPARQL queries. MuSe considers the type of frequently occuring triple patterns and optimizes RDF storage to answer such triple patterns in minimum time. It accesses only the tables that are sufficient for answering a triple pattern instead of scanning the whole RDF dataset. The extensive experiments on two synthetic RDF datasets i.e. LUBM and WatDiv, show that MuSe outperforms the compared state-of-the art frameworks in terms of query execution time and scalability.

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A comprehensive overview of RDF for spatial and spatiotemporal data management

Cited by 2 publications

References 72 publications

Discovering Links between Geospatial Data Sources in the Web of Data: The Open Geospatial Engine Approach

Discovering Links between Geospatial Data Sources in the Web of Data: The Open Geospatial Engine Approach

MuSe: a multi-level storage scheme for big RDF data using MapReduce

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