Sparklify: A Scalable Software Component for Efficient Evaluation of SPARQL Queries over Distributed RDF Datasets

Stadler, Claus; Sejdiu, Gëzim; Graux, Damien; Lehmann, Jens

doi:10.1007/978-3-030-30796-7_19

Cited by 19 publications

(20 citation statements)

References 15 publications

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“…In addition, we compare our proposed approach with selected state-of-the-art distributed SPARQL query evaluators. In particular, we compare our approach with SHARD [17] -the original approach implemented on Hadoop MapReduce, SPARQLGX [6]'s direct evaluator SDE, and Sparklify [21] and report the query execution time (cf. Table 2).…”

Section: Preliminary Resultsmentioning

confidence: 99%

“…Querying RDF data efficiently becomes challenging when the size of the data increases. This has motivated a considerable amount of work on designing distributed RDF systems able to efficiently evaluate SPARQL queries [6,20,21]. Being able to query a large amount of data in an efficient and faster way is one of the key requirements for every SPARQL engine.…”

Section: Introductionmentioning

confidence: 99%

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Towards a Scalable Semantic-Based Distributed Approach for SPARQL Query Evaluation

Sejdiu

Graux

Khan

et al. 2019

Lecture Notes in Computer Science

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Over the last two decades, the amount of data which has been created, published and managed using Semantic Web standards and especially via Resource Description Framework (RDF) has been increasing. As a result, efficient processing of such big RDF datasets has become challenging. Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. In this study, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets using a semantic-based partition and is implemented inside the state-of-the-art RDF processing framework: SANSA. An evaluation of the performance of our approach in processing large-scale RDF datasets is also presented. The preliminary results of the conducted experiments show that our approach can scale horizontally and perform well as compared with the previous Hadoopbased system. It is also comparable with the in-memory SPARQL query evaluators when there is less shuffling involved.

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Section: Preliminary Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards a Scalable Semantic-Based Distributed Approach for SPARQL Query Evaluation

Sejdiu

Graux

Khan

et al. 2019

Lecture Notes in Computer Science

Self Cite

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show abstract

“…Since SPARQL is not directly supported by these DBMS, a translation of SPARQL to DMBS-supportedlanguage is required for query execution. Most of the RDF engines in this category translate SPARQL to SQL using existing translation tools e.g., Sparklify [110], Ontop [18] etc.…”

Section: Sparql Translationmentioning

confidence: 99%

“…Since the data is vertical partitioned by using predicates of the triples, SANSA stack maintains a natural predicate-based index. Sparklify [110] is used as default query engine for SPARQL-to-SQL translation of SPARQL queries into Spark SQL to be executed on top of Apache Spark SQL engine.…”

Section: Distributed Rdf Enginesmentioning

confidence: 99%

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

Ali¹,

Saleem²,

Yao³

et al. 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 significant 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 various mechanisms to implement critical 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 critical for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a crucial 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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“…Semantic Analytics Stack (Sansa) [94], a data processing engine contains different layers for large scale analysis of RDF data. Sparklify [49] is one of a layer in the SANSA framework and serves as a default query engine for SPARQL-to-SQL translation of SPARQL queries into Apache Spark code through Spark SQL. Sparklify makes use of extended Vertical partitioning (VP) of RDF data.…”

Section: D-sparqmentioning

confidence: 99%

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

Ali¹,

Yao²,

Ngomo³

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Sparklify: A Scalable Software Component for Efficient Evaluation of SPARQL Queries over Distributed RDF Datasets

Cited by 19 publications

References 15 publications

Towards a Scalable Semantic-Based Distributed Approach for SPARQL Query Evaluation

Towards a Scalable Semantic-Based Distributed Approach for SPARQL Query 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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