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    • PRoST

      Public
      RDF storage and SPARQL processing on top of Apache Spark.
      Java
      MIT License
      72043Updated Oct 5, 2022Oct 5, 2022
    • A curated collection of resources on scholarly data analysis ranging from datasets, papers, and code about bibliometrics, citation analysis, and other scholarly commons resources.
      26000Updated Oct 23, 2019Oct 23, 2019
    • Java
      0000Updated Mar 15, 2019Mar 15, 2019
    • arxiv cs analysis
      JavaScript
      0100Updated Jan 15, 2019Jan 15, 2019
    • Python
      GNU General Public License v3.0
      01150Updated Nov 11, 2018Nov 11, 2018
    • WELT

      Public
      Java
      0100Updated Sep 26, 2018Sep 26, 2018
    • S2X

      Public
      S2X (SPARQL on Spark with GraphX) is a SPARQL query processor for Hadoop based on Spark GraphX. It combines graph-parallel abstraction of GraphX to implement the graph pattern matching part of SPARQL with data-parallel computation of Spark to build the results of other SPARQL operators.
      Java
      Apache License 2.0
      4100Updated Sep 4, 2017Sep 4, 2017
    • PigSPARQL

      Public
      Pig Latin is a high-level language developed at Yahoo! Research designed for data analysis tasks, which is automatically transformed into MapReduce jobs and executed in a Hadoop cluster. PigSPARQL is a translation from SPARQL 1.0 to Pig Latin, which allows to execute SPARQL queries on large RDF graphs with MapReduce.
      Java
      Apache License 2.0
      1100Updated Sep 4, 2017Sep 4, 2017
    • S2RDF

      Public
      S2RDF (SPARQL on Spark for RDF) is a SPARQL query processor for Hadoop based on Spark SQL. It uses the relational interface of Spark for query execution and comes with a novel partitioning schema for RDF called ExtVP (Extended Vertical Partitioning) that is an extension of the Vertical Partitioning (VP) schema introduced by Abadi et al. ExtVP en…
      Java
      Apache License 2.0
      6100Updated Sep 4, 2017Sep 4, 2017
    • Sempala

      Public
      Sempala is a SPARQL-over-SQL approach to provide interactive-time SPARQL query processing on Hadoop. It stores RDF data in a columnar layout (Parquet) on HDFS and uses either Impala or Spark as the execution layer on top of it. SPARQL queries are translated into Impala/Spark SQL for execution.
      Java
      Apache License 2.0
      2100Updated Sep 4, 2017Sep 4, 2017