RDFAdaptor: Efficient ETL Plugins for RDF Data Process

Li Jiao; Xian Guojian; Zhao Ruixue; Huang Yongwen; Kou Yuantao; Luo Tingting; Sun Tan

doi:10.2478/jdis-2021-0020

Journal of Data and Information Science (Apr 2021)

RDFAdaptor: Efficient ETL Plugins for RDF Data Process

Li Jiao,
Xian Guojian,
Zhao Ruixue,
Huang Yongwen,
Kou Yuantao,
Luo Tingting,
Sun Tan

Affiliations

Li Jiao: Agricultural Information Institute of CAAS, Beijing100081, China
Xian Guojian: Agricultural Information Institute of CAAS, Beijing100081, China
Zhao Ruixue: Agricultural Information Institute of CAAS, Beijing100081, China
Huang Yongwen: Agricultural Information Institute of CAAS, Beijing100081, China
Kou Yuantao: Agricultural Information Institute of CAAS, Beijing100081, China
Luo Tingting: Agricultural Information Institute of CAAS, Beijing100081, China
Sun Tan: Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing100081, China

DOI: https://doi.org/10.2478/jdis-2021-0020
Journal volume & issue: Vol. 6, no. 3
pp. 123 – 145

Abstract

Read online

The interdisciplinary nature and rapid development of the Semantic Web led to the mass publication of RDF data in a large number of widely accepted serialization formats, thus developing out the necessity for RDF data processing with specific purposes. The paper reports on an assessment of chief RDF data endpoint challenges and introduces the RDF Adaptor, a set of plugins for RDF data processing which covers the whole life-cycle with high efficiency.

Published in Journal of Data and Information Science

ISSN: 2096-157X (Print); 2543-683X (Online)
Publisher: Sciendo
Country of publisher: Poland
LCC subjects: Technology: Technology (General): Industrial engineering. Management engineering: Information technology; Science: Mathematics: Instruments and machines: Electronic computers. Computer science
Website: https://sciendo.com/journal/JDIS

About the journal

Abstract

Keywords