An empirical performance evaluation of a semantic-based data retrieving process from RDBs & RDF data storages


  • Leandro Tabares Martín Universidad de las Ciencias Informáticas, Carretera a San Antonio de los Baños Km 2½, La Habana, Cuba.
  • Félix Oscar Fernández Peña Universidad Técnica de Ambato, Ambato, Ecuador.
  • Amed Abel Leiva Mederos Universidad Central “Marta Abreu” de las Villas, Villa Clara, Cuba.
  • Jyrki Nummenmaa Universidad de Tampere, Tampere, Finlandia.


SQL and, more recently, SPARQL are standard languages of the software industry for retrieving data. Other studies showed that retrieving data by using a SPARQL query is much slower than the semantically equivalent SQL query. Nevertheless, some recent proposals optimized database servers for SPARQL queries. This paper presents the results of a comparative analysis between the capability of SQL and SPARQL with respect to the retrieval of data from a relational database and RDF-triples. A free and open source-based scenario was constructed by using PostgreSQL and Virtuoso for storing data, and RETRI, a data retrieving software built in JavaScript which displays data views in a specific XML format. Open data from the British National Library Bibliographic Data Set were used in the experiment; results were analyzed from a performance perspective.
Keywords: Data views, retrieving process performance, semantic web.

SQL y más recientemente SPARQL, son lenguajes estándares para la recuperación de datos en la industria del software. Otros estudios han mostrado que recuperar datos utilizando consultas SPARQL es mucho más lento que su consulta semánticamente equivalente en SQL. Sin embargo, algunas propuestas recientes han optimizado servidores de bases de datos para consultas SPARQL. En este artículo se presentan los resultados de comparar SQL y SPARQL en cuanto a la recuperación de datos a partir de bases de datos relacionales y tripletas RDF respectivamente. Un escenario basado en software libre y de código abierto fue construido usando PostgreSQL y Virtuoso para almacenar datos y RETRI, un software para la recuperación de datos desarrollado en JavaScript, que muestra vistas de datos almacenadas en un formato XML específico. Se utilizaron datos abiertos del conjunto de datos de la Biblioteca Nacional Británica en el experimento y los resultados fueron analizados desde una perspectiva de rendimiento.
Palabras clave: Vistas de datos, rendimiento del proceso de recuperación, web semántica.


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Cómo citar

Tabares Martín, L., Fernández Peña, F. O., Leiva Mederos, A. A., & Nummenmaa, J. (2017). An empirical performance evaluation of a semantic-based data retrieving process from RDBs & RDF data storages. Maskana, 7(Supl.), 23–34. Recuperado a partir de