To further improve the efficiency of RDFS rules reasoning for large-scaled RDF data, this paper design a graph-based RDF data partitioning and storage schema ...

A graph-based RDF data partitioning and storage schema based on HBase is designed and a novel RDFS reasoning approach is proposed by exploiting the Spark ...

Distributed RDFS Rules Reasoning for Large-Scaled RDF Graphs Using Spark. In 9th International Conference on Service Science, ICSS 2016, ChongQing, China ...

Sep 30, 2019 · In this paper, we present RDFSpark: a new distributed RDF query management based on Spark to ensure scalability, fault tolerance, and performance.

Apache Spark [29] is an in-memory distributed computing platform designed for large-scale data processing. Spark was originally developed at UC Berkeley in 2009 ...

A scalable RDF data framework, which uses key-value store for offline RDF storage and pipelined in-memory based query strategy for online SPARQL queries and ...

Mar 29, 2019 · This thesis is organized as follows: Chapter 3 gives general background knowledge on semantic web, RDF data management, SPARQL query processing, ...

The main reasoning services are based on a query rewriting approach for SPARQL that benefits from an intelligent encoding of an extension of the RDFS (i.e., ...

We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial ...

Oct 9, 2024 · In this paper, we present DIAERESIS, a novel platform that accepts as input an RDF dataset and effectively partitions it, minimizing data access and improving ...