Large-Scale RDF Dataset Slicing

Marx, Edgard; Shekarpour, Saeedeh; Auer, Sören; Ngomo, Axel-Cyrille Ngonga

doi:10.1109/icsc.2013.47

Cited by 7 publications

(28 citation statements)

References 5 publications

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“…The previously introduced concept of RDF dataset slice [14], [15] focuses particularly on both the selection and extraction steps of the Linked Open Data consumption process. These steps are essential to reduce space and time complexity in the whole process since the retrieved fragment is a subset (i.e., a slice) of the original dataset.…”

Section: Introductionmentioning

confidence: 99%

“…It consists into devising the fragment of SPARQL dubbed eSliceSPARQL, which enables a more granular selection of slices of datasets fulfilling typical information needs. eSliceSPARQL is an extension of the previous introduced SliceSPARQL [14], [15] and supports multi-join graph patterns for which each connected sub-graph pattern involves a maximum of one variable or IRI in its join conditions. This restriction guarantees the efficient processing of the query against a sequential dataset dump stream.…”

Section: Introductionmentioning

confidence: 99%

“…This restriction guarantees the efficient processing of the query against a sequential dataset dump stream. As a result, Torpedo can perform a more granular data selection than the previous introduced RDF dataset slice methods [14], [15]. Our main contributions are as follows:…”

Section: Introductionmentioning

confidence: 99%

“…• We extend previous introduced SliceSPARQL operator [14], [15] to the eSliceSPARQL, which supports multijoin type queries and SPARQL FILTER operator.…”

Section: Introductionmentioning

confidence: 99%

“…• We compare SliceSPARQL with eSliceSPARQL in an extensive evaluation using DBpedia 2014 4 and show that it is around 26% faster than previous introduced RDF dataset slicing methods [14], [15].…”

Section: Introductionmentioning

confidence: 99%

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Torpedo: Improving the State-of-the-Art RDF Dataset Slicing

Marx

Shekarpour

Soru

et al. 2017

2017 IEEE 11th International Conference on Semantic Computing (ICSC)

Self Cite

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Abstract-Over the last years, the amount of data published as Linked Data on the Web has grown enormously. In spite of the high availability of Linked Data, organizations still encounter an accessibility challenge while consuming it. This is mostly due to the large size of some of the datasets published as Linked Data. The core observation behind this work is that a subset of these datasets suffices to address the needs of most organizations. In this paper, we introduce Torpedo, an approach for efficiently selecting and extracting relevant subsets from RDF datasets. In particular, Torpedo adds optimization techniques to reduce seek operations costs as well as the support of multi-join graph patterns and SPARQL FILTERs that enable to perform a more granular data selection. We compare the performance of our approach with existing solutions on nine different queries against four datasets. Our results show that our approach is highly scalable and is up to 26% faster than the current state-of-the-art RDF dataset slicing approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…• We extend previous introduced SliceSPARQL operator [14], [15] to the eSliceSPARQL, which supports multijoin type queries and SPARQL FILTER operator.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Torpedo: Improving the State-of-the-Art RDF Dataset Slicing

Marx

Shekarpour

Soru

et al. 2017

2017 IEEE 11th International Conference on Semantic Computing (ICSC)

Self Cite

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Interest-Based RDF Update Propagation

Endris

Faisal

Orlandi

et al. 2015

The Semantic Web - ISWC 2015

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Many LOD datasets, such as DBpedia and LinkedGeoData, are voluminous and process large amounts of requests from diverse applications. Many data products and services rely on full or partial local LOD replications to ensure faster querying and processing. While such replicas enhance the flexibility of information sharing and integration infrastructures, they also introduce data duplication with all the associated undesirable consequences. Given the evolving nature of the original and authoritative datasets, to ensure consistent and up-to-date replicas frequent replacements are required at a great cost. In this paper, we introduce an approach for interest-based RDF update propagation, which propagates only interesting parts of updates from the source to the target dataset. Effectively, this enables remote applications to 'subscribe' to relevant datasets and consistently reflect the necessary changes locally without the need to frequently replace the entire dataset (or a relevant subset). Our approach is based on a formal definition for graphpattern-based interest expressions that is used to filter interesting parts of updates from the source. We implement the approach in the iRap framework and perform a comprehensive evaluation based on DBpedia Live updates, to confirm the validity and value of our approach.

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Speeding up Publication of Linked Data Using Data Chunking in LinkedPipes ETL

Klímek

Škoda

2017

On the Move to Meaningful Internet Systems. OTM 2017 Conferences

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Large-Scale RDF Dataset Slicing

Cited by 7 publications

References 5 publications

Torpedo: Improving the State-of-the-Art RDF Dataset Slicing

Torpedo: Improving the State-of-the-Art RDF Dataset Slicing

Interest-Based RDF Update Propagation

Speeding up Publication of Linked Data Using Data Chunking in LinkedPipes ETL

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