A subscribable peer-to-peer RDF repository for distributed metadata management

Cai, Min; Frank, Martin; Yan, Baoshi; MacGregor, Robert M.

doi:10.1016/j.websem.2004.10.003

Cited by 65 publications

(59 citation statements)

References 3 publications

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“…RDFPeers [55] is a distributed infrastructure for managing large-scale sets of RDF data. It is based on the multi-attribute addressable network MAAN [56].…”

Section: Related Workmentioning

confidence: 99%

Scalable distributed indexing and query processing over Linked Data

Karnstedt

Sattler

Hauswirth

2012

Journal of Web Semantics

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“…RDFPeers [55] is a distributed infrastructure for managing large-scale sets of RDF data. It is based on the multi-attribute addressable network MAAN [56].…”

Section: Related Workmentioning

confidence: 99%

Scalable distributed indexing and query processing over Linked Data

Karnstedt

Sattler

Hauswirth

2012

Journal of Web Semantics

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“…Several research works [19,67,85,86] focus on accessing conventional relational databases using SPARQL, which requires the SPARQL-to-SQL query translation. Distributed techniques to RDF data management and querying are discussed in [26,62,87,103]. Finally, [18,54,55,92] propose benchmarks for Semantic Web data management systems.…”

Section: Storing and Querying Rdf Data Using Relational Rdf Storesmentioning

confidence: 99%

RDFProv: A relational RDF store for querying and managing scientific workflow provenance

Chebotko¹,

Lu²,

Fei³

et al. 2010

Data & Knowledge Engineering

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Provenance metadata has become increasingly important to support scientific discovery reproducibility, result interpretation, and problem diagnosis in scientific workflow environments. The provenance management problem concerns the efficiency and effectiveness of the modeling, recording, representation, integration, storage, and querying of provenance metadata. Our approach to provenance management seamlessly integrates the interoperability, extensibility, and inference advantages of Semantic Web technologies with the storage and querying power of an RDBMS to meet the emerging requirements of scientific workflow provenance management. In this paper, we elaborate on the design of a relational RDF store, called RDFPROV, which is optimized for scientific workflow provenance querying and management. Specifically, we propose: i) two schema mapping algorithms to map an OWL provenance ontology to a relational database schema that is optimized for common provenance queries; ii) three efficient data mapping algorithms to map provenance RDF metadata to relational data according to the generated relational database schema, and iii) a schema-independent SPARQL-to-SQL translation algorithm that is optimized on-the-fly by using the type information of an instance available from the input provenance ontology and the statistics of the sizes of the tables in the database. Experimental results are presented to show that our algorithms are efficient and scalable. The comparison with two popular relational RDF stores, Jena and Sesame, and two commercial native RDF stores, AllegroGraph and BigOWLIM, showed that our optimizations result in improved performance and scalability for provenance metadata management. Finally, our case study for provenance management in a real-life biological simulation workflow showed the production quality and capability of the RDFPROV system. Although presented in the context of scientific workflow provenance management, many of our proposed techniques apply to general RDF data management as well.

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“…Then, each triple of form (sub ject, predicate, ob ject) is stored in Atlas three times, once for its subject, once for its predicate and once for its object. This triple-indexing approach was originally introduced in [34]. Each of these storage operations is implemented using the underlying put operation provided by the Bamboo DHT where the key is the subject, predicate or object value respectively and the item is the triple itself.…”

Section: Atlas In Detailmentioning

confidence: 99%