D. Lanti, G. Xiao, und D. Calvanese. Proceedings of the ISWC~2016 Posters & Demonstrations Track. Co-located with the 15th International Semantic Web Conference (ISWC~2016), Volume 1690 von CEUR Electronic Workshop Proceedings, CEUR-WS.org, (2016)
Zusammenfassung
In this paper we present an experimental evaluation of VIG, a
data scaler for OBDA benchmarks. Data scaling is a relatively
recent approach, proposed in the database community, that
allows for quickly scaling an input data instance to s times
its size, while preserving certain application-specific
characteristics. The advantages of scaling are that the
generator is general, in the sense that it can be re-used on
different database schemas, and that users are not required
to manually input the data characteristics. VIG lifts the
scaling approach from the database level to the OBDA level,
where the domain information of ontologies and mappings has
to be taken into account as well. To evaluate the quality of
VIG, in this paper we use it to generate data for the Berlin
SPARQL Benchmark (BSBM), and compare it with the official
BSBM data generator.
%0 Conference Paper
%1 2016-ISWC-poster-vig
%A Lanti, Davide
%A Xiao, Guohui
%A Calvanese, Diego
%B Proceedings of the ISWC~2016 Posters & Demonstrations Track. Co-located with the 15th International Semantic Web Conference (ISWC~2016)
%D 2016
%I CEUR-WS.org
%K optique-project
%T An Evaluation of VIG with the BSBM Benchmark
%V 1690
%X In this paper we present an experimental evaluation of VIG, a
data scaler for OBDA benchmarks. Data scaling is a relatively
recent approach, proposed in the database community, that
allows for quickly scaling an input data instance to s times
its size, while preserving certain application-specific
characteristics. The advantages of scaling are that the
generator is general, in the sense that it can be re-used on
different database schemas, and that users are not required
to manually input the data characteristics. VIG lifts the
scaling approach from the database level to the OBDA level,
where the domain information of ontologies and mappings has
to be taken into account as well. To evaluate the quality of
VIG, in this paper we use it to generate data for the Berlin
SPARQL Benchmark (BSBM), and compare it with the official
BSBM data generator.
@inproceedings{2016-ISWC-poster-vig,
abstract = {In this paper we present an experimental evaluation of VIG, a
data scaler for OBDA benchmarks. Data scaling is a relatively
recent approach, proposed in the database community, that
allows for quickly scaling an input data instance to s times
its size, while preserving certain application-specific
characteristics. The advantages of scaling are that the
generator is general, in the sense that it can be re-used on
different database schemas, and that users are not required
to manually input the data characteristics. VIG lifts the
scaling approach from the database level to the OBDA level,
where the domain information of ontologies and mappings has
to be taken into account as well. To evaluate the quality of
VIG, in this paper we use it to generate data for the Berlin
SPARQL Benchmark (BSBM), and compare it with the official
BSBM data generator.},
added-at = {2016-11-02T04:00:58.000+0100},
audience = {academic},
author = {Lanti, Davide and Xiao, Guohui and Calvanese, Diego},
biburl = {https://www.bibsonomy.org/bibtex/20691386eae7f7aa01df2f7362e7b2049/calvanese},
booktitle = {Proceedings of the ISWC~2016 Posters {\&} Demonstrations Track. Co-located with the 15th International Semantic Web Conference (ISWC~2016)},
interhash = {d569fbbf98afd5ed0e325febb1714fa6},
intrahash = {0691386eae7f7aa01df2f7362e7b2049},
keywords = {optique-project},
partneroptique = {FUB},
publisher = {CEUR-WS.org},
series = {CEUR Electronic Workshop Proceedings},
timestamp = {2016-11-07T04:10:56.000+0100},
title = {An Evaluation of {VIG} with the {BSBM} Benchmark},
volume = 1690,
wpoptique = {WP6},
year = 2016,
yearoptique = {Y4}
}