Distance-Based Triple Reordering for SPARQL Query Optimization

Meimaris, Marios; Papastefanatos, George

doi:10.1109/icde.2017.227

“…, t n−1 } to avoid costly Cartesian products. Aside from considering the cardinality of triple patterns, Meimaris and Papastefanatos [131] propose a distance-based planning, where pairs of triple patterns with more overlapping nodes and more similar cardinality estimates have lesser distance between them; the query planner then tries to group and join triple patterns with the smallest distances first in a greedy manner. Greedy strategies may not, however, always provide the best ordering (see, e.g., Figure 17).…”

Section: Join Reorderingmentioning

confidence: 99%

A survey of RDF stores & SPARQL engines for querying knowledge graphs

Ali

¹

,

Saleem

²

,

Yao

³

et al. 2021

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Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graphbased data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

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“…Query Pr. T Q V P G M T Q E P N J S M P M W N R Q RedLand [22] 2001 ✓ ✓ Jena [128] 2002 ✓ ✓ ✓ ✓ RDF4J [34] 2002 ✓ ✓ ✓ ✓ ✓ RSSDB [97,98] 2002 ✓ ✓ ✓ ✓ 3store [74] 2003 [45,46] 2004 [121] 2004 ✓ ✓ ✓ ✓ BRAHMS [90] 2005 ✓ ✓ ✓ GraphDB [105,26] 2005 [179] 2006 ✓ ✓ ✓ ✓ ✓ Virtuoso [54] 2006 ✓ ✓ ✓ ✓ ✓ ✓ GRIN [194] 2007 ✓ ✓ ✓ SW-Store [1] 2007 ✓ Blazegraph [190] 2008 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Hexastore [201] 2008 ✓ ✓ ✓ ✓ RDF-3X [142] 2008 ✓ ✓ ✓ ✓ ✓ BitMat [15] 2009 ✓ ✓ ✓ ✓ DOGMA [33] 2009 ✓ ✓ ✓ ✓ LuposDate [61] 2009 ✓ ✓ ✓ Parliament [106] 2009 [223] 2011 ✓ ✓ ✓ ✓ SpiderStore [138] 2011 ✓ ✓ SAINT-DB [154] 2012 [191] 2013 [11] 2015 ✓ ✓ ✓ RDFCSA [32,31] 2015 ✓ ✓ ✓ RDFox [138] 2015 ✓ ✓ ✓ ✓ ✓ Turbo HOM++ [102] 2015 ✓ ✓ ✓ RIQ [100] 2016 ✓ ✓ ✓ ✓ ✓ axonDB [131] 2017 ✓ ✓ ✓ ✓ ✓ HTStore [116] 2017 ✓ ✓ AMBER [87] 2018 ✓ Jena-LTJ [81] 2019 ✓ ✓ ✓ BMatrix [30] 2020 ✓ ✓ Tentris [24] 2020 ✓ ✓ ✓ ✓ which are additionally associated with a triple identifier. SPARQL queries are supported, where the most recent version provides an option for two query engines: SBQE, which is optimized for SPARQL 1.0-style queries, and MJQE, which features merge joins and caching techniques optimized for property paths.…”

Section: Enginementioning

confidence: 99%

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Ali¹,

Yao²,

Hogan³

et al. 2021

Preprint

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Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graphbased data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

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“…Kalayci et al [33] presented a new solution for the optimization using Ant Colony Optimization (ACO) and reordering triple patterns and selectivity estimation introduced by [26] with some modification. Meimaris and Papastefanatos [34] presented a new approach of join reordering that converts a query into a multidimensional vector space and performs distance-based optimisation.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Different Queries using Optimization Algorithm (DE)

Saharan¹,

Lather²,

Radhakrishnan³

2018

IJCNIS

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The biggest challenge in modern web is to tackle tremendous growth of data, scattered and continuously updating in nature. Processing of such unscattered data by human or machine remains a tedious task. Semantic Web; as a solution has already been invented. But, still there are some other challenges, like as optimization of the query. We introduce a new approach for real-time SPARQL query optimization with different forms and different triple patterns. The strategy introduces rearrangement of order of triple pattern using Differential Evolution(DE). The experimental study focus on main-memory model of RDF data and ARQ query engine of Jena. We compare the result of proposed approach with the Ant Colony Optimization(ACO) different versions and some other approaches. Results shows that proposed approach provides better execution time as compare to the other approaches.

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Distance-Based Triple Reordering for SPARQL Query Optimization

Cited by 11 publications

References 9 publications

A survey of RDF stores & SPARQL engines for querying knowledge graphs

A survey of RDF stores & SPARQL engines for querying knowledge graphs

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Optimization of Different Queries using Optimization Algorithm (DE)

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