DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data

Jabeen, Hajira; Haziiev, Eskender; Sejdiu, Gëzim; Lehmann, Jens

doi:10.1109/icsc.2020.00079

Cited by 8 publications

(10 citation statements)

References 20 publications

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“…The distributed RDF engines can be divided into four broader categories : (1) No-SQL-based, (2) Hadoop/Sparkbased, (3) Distributed memory-based, and (4) others, e.g., Table 10: Categorization of distributed RDF Engines. Storage (T = Triple Table, P = Property Table, V [76] CliqueSquare [34] TripleRush [113] chameleon-db [8] Partout [33] Sempala [104] TriAD [39] SparkRDF [19] SemStore [124] DREAM [40] DiploCloud [126] SPARQLGX [103] S2RDF [105] AdPart [41] S2X [102] gStoreD [86] Wukong [108] SANSA [69] Stylus [48] Koral [55] PRoST [24] WORQ [72] Anzograph 38 Neptune 39 HF,VF,MF [84] DiStRDF [120] Leon [38] DISE [52] MPI-based, Graph-based. Our discussion is focused towards the storage, indexing, query processing, and partitioning in state-of-the-art distributed RDF engines.…”

Section: Distributed Rdf Enginesmentioning

confidence: 99%

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

Ali¹

2020

Preprint

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Section: Distributed Rdf Enginesmentioning

confidence: 99%

“…DISE [52] stores RDF data as a 3D tensor in which the adjacency matrix of subject, predicate and object is known as slice. According to [65] Tensors can be shown as a multidimensional array of ordered columns.…”

Section: Distributed Rdf Enginesmentioning

confidence: 99%

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

Ali¹

2020

Preprint

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show abstract

“…DISE [52] stores RDF data as 3D tensors, a multidimensional array of ordered columns . It performs the translation of SPARQL queries into Spark tensor operation through Spark-Scala compliant code.…”

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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“…DISE [102] stores RDF data as tensors. RDF triples can be represented as a 3D tensor in which their slices represents an adjacency matrix of subject, predicate and object.…”

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

DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data

Cited by 8 publications

References 20 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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