Spider

Abstract

RDF is a data model for representing labeled directed graphs, and it is used as an important building block of semantic web. Due to its flexibility and applicability, RDF has been used in applications, such as semantic web, bioinformatics, and social networks. In these applications, large-scale graph datasets are very common. However, existing techniques are not effectively managing them. In this paper, we present a scalable, efficient query processing system for RDF data, named SPIDER, based on the well-known parallel/distributed computing framework, Hadoop. SPIDER consists of two major modules (1) the graph data loader, (2) the graph query processor. The loader analyzes and dissects the RDF data and places parts of data over multiple servers. The query processor parses the user query and distributes sub queries to cluster nodes. Also, the results of sub queries from multiple servers are gathered (and refined if necessary) and delivered to the user. Both modules utilize the MapReduce framework of Hadoop. In addition, our system supports some features of SPARQL query language. This prototype will be foundation to develop real applications with large-scale RDF graph data.

An Evaluation and Comparative study of massive RDF Data management approaches based on Big Data Technologies

“…Recently, much of the research is focused on the use of Big Data technologies for managing large volumes of Semantic Web data, we mention Jena-HBase [1], Rainbow [2], Hive-HBase [3], MapReduce-HBase [4], CumulusRDF [5], Rya [6] , H2RDF [7], SPIDER [8], SHARD [10]. Therefore, this paper presents our work that compares and evaluates the NoSQL stores for RDF data management, the first work done by Mauroux et al [9], but it has only evaluated five RDF stores based on NoSQL: CumulusRDF, 4store, Hive+HBase, Jena + HBase, and Couchbase [9], using standard RDF benchmarks on two deployments modes single-machine and distributed deployments.…”

“…Now, we review some of RDF systems which are based on NoSQL databases for storing a massive RDF data including : Jena-HBase [1], Rainbow [2], Hive-HBase [3], MapReduce-HBase [4], CumulusRDF [5], Rya [6] , H2RDF [7], SPIDER [8], Couchbase [9], SHARD [10]. The comparison is based on some criteria of database software such as :NoSQL database, NoSQL database model, database Licence, Index structure, Querying, Index triples, join optimization method, SPARQL Translate, and the Execution time.…”

A parallel approach for improving Geo-SPARQL query performance

“…Recently, a number of studies have addressed the implementation of distributed SPARQL query engines using the Map-Reduce implementations such as the Hadoop framework (e.g. Husain et al 2009;Choi et al 2009;Kulkarni 2010).…”

SPARQL2Flink: Evaluation of SPARQL Queries on Apache Flink

“…The amount and size of datasets represented in the Resource Description Framework (RDF) [1] language are increasing; this leads to challenging the limits of existing triple stores and SPARQL query evaluation technologies, requiring more efficient query evaluation techniques. Several proposals have been documented in state of the art use of Big Data technologies for storing and querying RDF data [2][3][4][5][6]. Some of these proposals have focused on executing SPARQL queries on the MapReduce Programming Model [7] and its implementation, Hadoop [8].…”