Distributed subgraph query for RDF graph data based on MapReduce

Su, Qianxiang; Huang, Qingrong; Wu, Nan; Pan, Ying

doi:10.1016/j.compeleceng.2022.108221

Cited by 2 publications

(1 citation statement)

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“…Pregel computations consist of a sequence of iterations, called supersteps. Parallels such as MapReduce [19][20][21], which is a programming model for processing large amounts of data in distributed parallel computing, simplify the design and implementation of large-capacity data processing systems; however, they may result in inefficient processing because they do not support efficient data mining and machine learning algorithms. To resolve this problem, GraphLab has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Distributed Subgraph Query Processing Using Filtering Scores on Spark

Bok,

Kim,

Lee

et al. 2023

Electronics

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As various services have been generating large-scale graphs to represent multiple relationships between objects, studies have been conducted to obtain subgraphs with particular patterns. In this paper, we propose a distributed query processing method to efficiently search a subgraph for a large graph on Spark. To reduce unnecessary processing costs, the search order is determined by filtering scores using the probability distribution. The partitioned queries are searched in parallel in the distributed graph of each slave node according to the search order, and the local search results obtained from each slave node are combined and returned. The query is partitioned in triplets based on the determined search order. The performance of the proposed method is compared with the performance of existing methods to demonstrate its superiority.

show abstract

Section: Introductionmentioning

confidence: 99%

Distributed Subgraph Query Processing Using Filtering Scores on Spark

Bok,

Kim,

Lee

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

Distributed Subgraph Query Processing Using Filtering Scores on Spark

Bok¹,

Kim²,

Lee³

et al. 2023

Preprint

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As various services have been generating large scale graphs to represent multiple relationships between objects, studies have been conducted to obtain subgraphs with particular patterns. In this paper, we propose a distributed query processing method to efficiently search a subgraph for a large graph on Spark. To reduce unnecessary processing costs, the search order is determined by filtering scores using the probability distribution. The partitioned queries are searched in parallel in the distributed graph of each slave node according to the search order, and the local search results obtained from each slave node are combined and returned. The query is partitioned in triplets based on the determined search order. The performance of the proposed method is compared with the performance of existing methods to demonstrate its superiority.

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Distributed subgraph query for RDF graph data based on MapReduce

Cited by 2 publications

References 8 publications

Distributed Subgraph Query Processing Using Filtering Scores on Spark

Distributed Subgraph Query Processing Using Filtering Scores on Spark

Distributed Subgraph Query Processing Using Filtering Scores on Spark

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