Accelerating Partial Evaluation in Distributed SPARQL Query Evaluation

Peng, Peng; Zou, Lei; Guan, Runyu

doi:10.1109/icde.2019.00019

Cited by 21 publications

(9 citation statements)

References 26 publications

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“…The partition-based approaches [18,22,26,37,38,46,49,50] partition a large RDF graph into several fragments and distribute those fragments at different sites. Each remote site hosts its own centralized RDF storage and querying mechanism.…”

Section: Partition-based Approachesmentioning

confidence: 99%

Efficient distributed path computation on RDF knowledge graphs using partial evaluation

Mehmood

Saleem²,

Jha

et al. 2021

World Wide Web

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A key property of Linked Data is the representation and publication of data as interconnected labelled graphs where different resources linked to each other form a network of meaningful information. Searching these important relationships between resourceswithin single or distributed graphs -can be reduced to a pathfinding or navigation problem, i.e., looking for chains of intermediate nodes. SPARQL1.1, the current standard query language for RDF-based Linked Data defines a construct -called Property Paths (PPs) -to navigate between entities within a single graph. Since Linked Data technologies are naturally aimed at decentralised scenarios, there are many cases where centralising this data is not feasible or even not possible for querying purposes. To address these problems, we propose a SPARQL PP-based graph processing approach -dubbed DpcLD -where users can execute SPARQL PP queries and find paths distributed across multiple, connected graphs exposed as SPARQL endpoints. To execute the distributed path queries we implemented an index-free, cache-based query engine that communicates with a shared algorithm running on each remote endpoint, and computes the distributed paths. In this paper, we highlight the way in which this approach exploits and aggregates partial paths, within a distributed environment, to produce complete results. We perform extensive experiments to demonstrate the performance of our approach on two datasets: One representing 10 million triples from the DBPedia SPARQL benchmark, and another full benchmark dataset corresponding to 124 million triples. We also perform a scalability test of our approach using real-world genomics datasets distributed across multiple endpoints. We compare our distributed approach with other distributed and centralized pathfinding approaches, showing that it outperforms other distributed approaches by orders of magnitude, and provides a good trade-off for cases when the data cannot be centralised.

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Section: Partition-based Approachesmentioning

confidence: 99%

Efficient distributed path computation on RDF knowledge graphs using partial evaluation

Mehmood

Saleem²,

Jha

et al. 2021

World Wide Web

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“…In distributed assembly, local matches are joined in parallel at different sites. The work in [85] proposes some optimizations for partial evaluation and assembly for pruning useless intermediate results.…”

Section: Distributed Rdf Enginesmentioning

confidence: 99%

Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

Ali¹

2020

Preprint

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“…In distributed assembly, local matches are joined in parallel at different sites. The work in [113] proposes some optimizations for partial evaluation and assembly for pruning useless intermediate results.…”

Section: D-sparqmentioning

confidence: 99%

Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

Ali¹,

Yao²,

Ngomo³

2020

Preprint

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The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is a huge adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ different mechanisms to implement key components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is key for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a key optimization component of the RDF storage solutions. Finally, query execution involving different join orders significantly affects the query response time. This paper provides a comprehensive review of centralized and distributed RDF engines in terms of storage, indexing, language support, and query execution.

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Accelerating Partial Evaluation in Distributed SPARQL Query Evaluation

Cited by 21 publications

References 26 publications

Efficient distributed path computation on RDF knowledge graphs using partial evaluation

Efficient distributed path computation on RDF knowledge graphs using partial evaluation

Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

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