On data placement strategies in distributed RDF stores

Janke, Daniel; Staab, Steffen; Thimm, Matthias

doi:10.1145/3066911.3066915

Cited by 12 publications

(14 citation statements)

References 15 publications

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“…• Subject-hashed. In this technique hash function is applied on the subject of the triple and based on the output value, subject is assigned to a partition [56].This causes imbalance among partitions. This imbalance is shown in example given in Figure 12.…”

Section: Rdf Graph Partitioningmentioning

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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Section: Rdf Graph Partitioningmentioning

confidence: 99%

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

Ali¹,

Saleem²,

Yao³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…• URI Hierarchy-hashed: This partitioning is inspired by two assumptions: (1) IRIs have path hierarchy, (2) IRIs with a common hierarchy prefix are often queried together [54]. This partitioning technique extracts path hierarchies from the IRIs and assigning triples having the same hierarchy prefixes into one partition.…”

Section: Fig 11 Types Of Partitioning Used Rdf Enginesmentioning

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.

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“…Year Central Focus Janke et al [57] 2020 Partitioning, indexing, and query evaluation in distributed RDF engines Santana et al [100] 2020 Mapping strategies for storage of RDF based data into the NoSQL databases Alaoui [5] 2019 General categorization of different RDF engines Janke et al [54] 2018 Partitioning, indexing, and query evaluation in distributed and cloud RDF engines Sakr et. al [92] 2018 Storage and query evaluation in centralized and distributed RDF engines Wylot et.…”

Section: Studymentioning

confidence: 99%

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

Ali¹

2020

Preprint

View full text Add to dashboard Cite

show abstract

On data placement strategies in distributed RDF stores

Cited by 12 publications

References 15 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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