Scalable SPARQL querying using path partitioning

Wu, Buwen; Zhou, Yongluan; Yuan, Pingpeng; Liu, Ling; Jin, Hai

doi:10.1109/icde.2015.7113334

“…In Sedge [142] a reimplementation of Pregel [84] is used to process distributed queries. A more common approach is, to use Hadoop to process only distributed joins as done in [63], [39], [143], SPA [81], VB-Partitioner [79], SHAPE [80] and [137].…”

Section: Distributed Rdf Storesmentioning

confidence: 99%

Storing and Querying Semantic Data in the Cloud

Janke

¹

,

Staab

²

2018

Lecture Notes in Computer Science

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In the last years, huge RDF graphs with trillions of triples were created. To be able to process this huge amount of data, scalable RDF stores are used, in which graph data is distributed over compute and storage nodes for scaling efforts of query processing and memory needs. The main challenges to be investigated for the development of such RDF stores in the cloud are: (i) strategies for data placement over compute and storage nodes, (ii) strategies for distributed query processing, and (iii) strategies for handling failure of compute and storage nodes. In this manuscript, we give an overview of how these challenges are addressed by scalable RDF stores in the cloud. 8 We adapted the definition of an RDF molecule in [38] to allow for paths with a length ≥ 1. 9 The term anchor vertex was taken from [79]. 10 dom(µ) refers to the set of variables of this binding.

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“…In Sedge [142] a reimplementation of Pregel [84] is used to process distributed queries. A more common approach is, to use Hadoop to process only distributed joins as done in [63], [39], [143], SPA [81], VB-Partitioner [79], SHAPE [80] and [137].…”

Section: Distributed Rdf Storesmentioning

confidence: 99%

Storing and Querying Semantic Data in the Cloud

Staab¹,

Janke²

2018

EasyChair Preprints

0

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In the last years, huge RDF graphs with trillions of triples were created. To be able to process this huge amount of data, scalable RDF stores are used, in which graph data is distributed over compute and storage nodes for scaling efforts of query processing and memory needs. The main challenges to be investigated for the development of such RDF stores in the cloud are: (i) strategies for data placement over compute and storage nodes, (ii) strategies for distributed query processing, and (iii) strategies for handling failure of compute and storage nodes. In this manuscript, we give an overview of how these challenges are addressed by scalable RDF stores in the cloud.

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“…Nevertheless, SPA suffers from a large amount of data duplication problem. Wu et al [13] introduce a path strategy-based method, which decomposes the RDF graph into the end-to-end paths. Then such paths are considered as the finest partitioning elements to realize the partitioning.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, RDF data partitioning is a vital technique for high efficient cloud storage. Recently, many RDF data partitioning methods have been proposed [4,[8][9][10][11][12][13]. Most of them are based on graph partitioning that usually distributes closely related nodes to the same storage node.…”

Section: Introductionmentioning

confidence: 99%

BRGP: a balanced RDF graph partitioning algorithm for cloud storage

Leng

¹

,

Chen

²

,

Zhong

³

et al. 2016

Concurrency and Computation

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Summary The continuous growth of resource description framework (RDF) data poses an important challenge on RDF data partitioning that is a vital technique for effective cloud storage. Recently, many partitioning algorithms for large RDF data have been developed, and most of them are based on graph partitioning. However, existing graph partitioning methods could not partition asymmetric RDF data effectively, resulting in a lower performance for cloud storage. This paper proposes a balanced RDF graph partitioning algorithm for storing massive RDF data on cloud. We first devise a modularity‐based multi‐level label propagation algorithm (MMLP) to partition RDF graph roughly and then use a balanced K‐mediods clustering algorithm for final k‐way partitioning. Balanced RDF graph partitioning algorithm designs an effective label update rule and a balanced modification strategy to achieve a high quality coarsening result and make the partition as equilibrium as possible. Experiments are carried on two representative RDF benchmarks and one real RDF dataset by comparison with two representative graph partitioning methods, that is, METIS and MLP+METIS. Results demonstrate that our proposed scheme can produce a high‐quality partition for massive RDF data storage on cloud. Copyright © 2016 John Wiley & Sons, Ltd.

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Scalable SPARQL querying using path partitioning

Cited by 36 publications

References 20 publications

Storing and Querying Semantic Data in the Cloud

Storing and Querying Semantic Data in the Cloud

Storing and Querying Semantic Data in the Cloud

BRGP: a balanced RDF graph partitioning algorithm for cloud storage

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