WORQ: Workload-Driven RDF Query Processing

Madkour, Amgad; Aly, A. A.; Aref, Walid G.

doi:10.1007/978-3-030-00671-6_34

“…Dictionary encoding with 64bit integers is used for compressed representation of RDF triples. The query optimizer of Bigdata generates optimized query execution plans according to two different approaches: the Table, V [46] SHARD [90] 4store [43] Virtuoso [30] HDT [32] H-RDF-3X [50] GraphDB 37 CumulusRDF [44] Rapid+ [89] Jena-HBase [62] Rya [86] AMADA [9] H2RDF [83] SHAPE [67] WARP [49] PigSPARQL [102] EAGRE [132] H2RDF+ [82] Trinity.RDF [131] D-SPARQ [75] CliqueSquare [34] TripleRush [113] chameleon-db [8] Partout [33] Sempala [103] TriAD [39] SparkRDF [19] SemStore [123] DREAM [40] DiploCloud [125] SPARQLGX [102] S2RDF [104] AdPart [41] S2X [101] gStoreD [85] Wukong [108] SANSA [68] Stylus [48] Koral [55] PRoST [24] WORQ [71] Anzograph 38 Neptune 39 HF,VF,MF [84] DiStRDF [119] Leon…”

Section: Distributed Rdf Enginesmentioning

confidence: 99%

“…WORQ [71] uses a query workload driven approach to partition the given RDF data among multiple data nodes which stores data in a Vertical partitioning manner. It uses Bloom Filters as indexes over the Vertically partitioned tables.…”

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.

show abstract

“…WORQ [98] performs the partition of RDF data in online fashion. WORK uses a query workload driven approach during the partitioning of data stored in a Vertical partitioning manner.…”

Section: D-sparqmentioning

confidence: 99%

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

Ali¹,

Yao²,

Ngomo³

2020

Preprint

3

0

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

“…WORQ [99] performs the partition of RDF data in online fashion. WORK uses a query workload driven approach during the partitioning of data stored in a Vertical partitioning manner.…”

Section: D-sparqmentioning

confidence: 99%

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

Ali¹,

Yao²,

Ngomo³

2020

Preprint

1

0

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

WORQ: Workload-Driven RDF Query Processing

Cited by 17 publications

References 24 publications

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

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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