Scalable Transformation of Big Geospatial Data into Linked Data

被引：1
|
作者：
Mandilaras, George [1 ]
Koubarakis, Manolis [1 ]
机构：
[1] Natl & Kapodistrian Univ Athens, Dept Informat & Telecommun, Athens, Greece
来源：
SEMANTIC WEB - ISWC 2021 | 2021年 / 12922卷
基金：
欧盟地平线“2020”;
关键词：
D O I：
10.1007/978-3-030-88361-4_28
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：480 / 495
页数：16
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