Discovering Critical Factors Affecting
RDF Stores Success
Gianfranco E. Modoni and Marco Sacco
Abstract Technologies for the effective and efficient handling of RDF data are one
of the main success factors for a larger scale take-up of Semantic Web Technologies
in real scenarios. In this regard, several software components (RDF Stores) devoted
to the semantic data persistence and retrieval are available in literature. However,
each of them may be appropriate and usable for some kinds of tasks and not for
others, and a one-size-fits-all killer application for this type of solutions is still not
(and probably will never be) available. The large number of available solutions and
the lack of widely accepted benchmarks for their rigorous evaluation do not help the
selection and the adoption of an appropriate RDF store compliant with the identified
needs of a specific case study. In order to contribute to fill this gap, a methodological
approach to evaluate and rank the relevant features of the RDF stores is presented in
this paper. Such an approach can help on one hand other researchers to discover the
factors affecting the success of the RDF stores and the other hand software architects
to select which RDF stores best fits the requirements of a certain application scenario.
1 Introduction
Semantic Web Technologies (SWT) [2] are increasingly being adopted to model
data (and knowledge) in a variety of fields such as, for example, manufacturing,
biology, medicine and healthcare, and Public Administration. In the growing landscape of “Polyglot Persistence” where enterprises exploit multiple technologies for
data management [31], SWT can play a key role to aggregate and integrate heterogeneous data distributed across many sources. This is due to their aptitude to enhance
G. E. Modoni (B)
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing,
National Research Council of Italy, via Lembo 38F, Bari, Italy
e-mail: gianfranco.modoni@stiima.cnr.it
M. Sacco
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing,
National Research Council of Italy, via Previati 1/E, Lecco, Italy
e-mail: marco.sacco@stiima.cnr.it
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_8
193
RDF Stores Success
Gianfranco E. Modoni and Marco Sacco
Abstract Technologies for the effective and efficient handling of RDF data are one
of the main success factors for a larger scale take-up of Semantic Web Technologies
in real scenarios. In this regard, several software components (RDF Stores) devoted
to the semantic data persistence and retrieval are available in literature. However,
each of them may be appropriate and usable for some kinds of tasks and not for
others, and a one-size-fits-all killer application for this type of solutions is still not
(and probably will never be) available. The large number of available solutions and
the lack of widely accepted benchmarks for their rigorous evaluation do not help the
selection and the adoption of an appropriate RDF store compliant with the identified
needs of a specific case study. In order to contribute to fill this gap, a methodological
approach to evaluate and rank the relevant features of the RDF stores is presented in
this paper. Such an approach can help on one hand other researchers to discover the
factors affecting the success of the RDF stores and the other hand software architects
to select which RDF stores best fits the requirements of a certain application scenario.
1 Introduction
Semantic Web Technologies (SWT) [2] are increasingly being adopted to model
data (and knowledge) in a variety of fields such as, for example, manufacturing,
biology, medicine and healthcare, and Public Administration. In the growing landscape of “Polyglot Persistence” where enterprises exploit multiple technologies for
data management [31], SWT can play a key role to aggregate and integrate heterogeneous data distributed across many sources. This is due to their aptitude to enhance
G. E. Modoni (B)
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing,
National Research Council of Italy, via Lembo 38F, Bari, Italy
e-mail: gianfranco.modoni@stiima.cnr.it
M. Sacco
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing,
National Research Council of Italy, via Previati 1/E, Lecco, Italy
e-mail: marco.sacco@stiima.cnr.it
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_8
193