EAGRE: Towards scalable I/O efficient SPARQL query evaluation on the cloud

Zhang, Xiaofei; Chen, Lei; Tong, Yongxin; Wang, Min

doi:10.1109/icde.2013.6544856

Cited by 27 publications

(9 citation statements)

References 29 publications

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“…minimal data transport) for efficient query processing -even in a commodity network environment (1 GB/s). Furthermore, our analysis revealed that previous experiments like [11], [16] and [12] suffered from a setting with highly inefficient methods for data transfer (i.e. based on the Hadoop/HDFS infrastructure) (see [18]).…”

Section: Introductionmentioning

confidence: 97%

“…In order to reduce the number of transferred intermediate results, hierarchical hash has been proposed as an extension of the hash cover strategy that computes the hash only on IRI prefixes [9]. Another commonly used graph cover strategy is the minimal edge-cut cover that assigns vertices to similarly-sized partitions in a way that the number of edges connecting vertices assigned to different partitions is minimised (e. g., used by [10][11][12]). Fur-thermore, the vertical cover strategy is inspired by relational databases.…”

Section: Introductionmentioning

confidence: 99%

“…It is a commonly held belief that query completion is optimized by approaches that emphasize local computation such as minimal edge-cut (cf. [11,12,16]). The first major contribution of this paper is to challenge this assumption by new experiments.…”

Section: Introductionmentioning

confidence: 99%

“…In order to determine to which extent graph cover strategies lead to efficient query answering, they have to be implemented and evaluated in distributed RDF stores. For instance, [8] and [12] evaluate various RDF stores that use different graph cover strategies, but these evaluations compared the RDF stores as wholes. Thus, their results also reflect the effects of, e. g., the different indexing strategies and persistence strategies (i. e., main memory vs. hard disk) used by the different stores.…”

Section: Introductionmentioning

confidence: 99%

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Impact analysis of data placement strategies on query efforts in distributed RDF stores

Janke

Staab

Thimm

2018

Journal of Web Semantics

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In the last years, scalable RDF stores in the cloud have been developed, where graph data is distributed over compute and storage nodes for scaling efforts of query processing and memory needs. One main challenge in these RDF stores is the data placement strategy that can be formalized in terms of graph covers. These graph covers determine whether (a) the triples distribution is well-balanced over all storage nodes (storage balance) (b) different query results may be computed on several compute nodes in parallel (vertical parallelization) and (c) individual query results can be produced only from triples assigned to few-ideally one-storage node (horizontal containment). We analyse the impact of three most commonly used graph cover strategies in these terms and found out that balancing query workload reduces the query execution time more than reducing data transfer over network. To this end, we present our novel benchmark and open source evaluation platform Koral.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact analysis of data placement strategies on query efforts in distributed RDF stores

Janke

Staab

Thimm

2018

Journal of Web Semantics

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“…Additionally, in [11,25,26] the authors study the structure of the data and provide structural summaries or representative schemas. None of these works is based on a given schema, and thus they require an extensive data scan.…”

Section: Related Workmentioning

confidence: 99%

Selectivity Estimation for SPARQL Triple Patterns with Shape Expressions

Abbas

Genevès

Roisin

et al. 2018

Lecture Notes in Computer Science

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ShEx (Shape Expressions) is a language for expressing constraints on RDF graphs. In this work we optimize the evaluation of conjunctive SPARQL queries, on RDF graphs, by taking advantage of ShEx constraints. Our optimization is based on computing and assigning ranks to query triple patterns, dictating their order of execution. We first define a set of well formed ShEx schemas, that possess interesting characteristics for SPARQL query optimization. We then define our optimization method by exploiting information extracted from a ShEx schema. We finally report on evaluation results performed showing the advantages of applying our optimization on the top of an existing state-of-the-art query evaluation system.

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Storing and Querying Semantic Data in the Cloud

Janke

Staab

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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EAGRE: Towards scalable I/O efficient SPARQL query evaluation on the cloud

Cited by 27 publications

References 29 publications

Impact analysis of data placement strategies on query efforts in distributed RDF stores

Impact analysis of data placement strategies on query efforts in distributed RDF stores

Selectivity Estimation for SPARQL Triple Patterns with Shape Expressions

Storing and Querying Semantic Data in the Cloud

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