Presto-RDF: SPARQL querying over big RDF data

Mulugeta Mammo, Srividya Bansal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


There has been a rapid increase in the amount of Resource Description Framework (RDF) data on the web. The processing of large volumes of RDF data requires an efficient storage and query-processing engine that can scale well with the volume of data. In the past two and half years, however, heavy users of big data systems, like Facebook, noted limitations with the query performance of these big data systems and began to develop new distributed query engines for big data that do not rely on map-reduce. Facebook’s Presto is one such example. This paper proposes an architecture based on Presto, called Presto-RDF, that can be used to process big RDF data. An evaluation of performance of Presto in processing big RDF data against Apache Hive is also presented. The results of the experiments show that Presto-RDF framework has a much higher performance than Apache Hive and native RDF store - 4store and it can be used to process big RDF data.

Original languageEnglish (US)
Title of host publicationDatabases Theory and Applications - 26th Australasian Database Conference, ADC 2015, Proceedings
EditorsMuhammad Aamir Cheema, Jianzhong Qi, Mohamed A. Sharaf
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319195476
StatePublished - 2015
Event26th Australasian Database Conference, ADC 2015 - Melbourne, Australia
Duration: Jun 4 2015Jun 7 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other26th Australasian Database Conference, ADC 2015


  • Database performance
  • Evaluation
  • Querying
  • Semantic web data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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