Optimizing Aggregate SPARQL Queries Using Materialized RDF Views

Dilshod Ibragimov; Katja Hose; Torben Bach Pedersen; Esteban Zimányi

doi:10.1007/978-3-319-46523-4_21

Optimizing Aggregate SPARQL Queries Using Materialized RDF Views

Dilshod Ibragimov, Katja Hose, Torben Bach Pedersen, Esteban Zimányi

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

17 Citations (Scopus)

Abstract

During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing techniques. To overcome these limitations, materialized views can be created and used as a source of precomputed partial results during query processing. However, materialized view techniques as proposed for relational databases do not support RDF specifics, such as incompleteness and the need to support implicit (derived) information. To overcome these challenges, this paper proposes MARVEL (MAterialized Rdf Views with Entailment and incompLetness). The approach consists of a view selection algorithm based on an associated RDF-specific cost model, a view definition syntax, and an algorithm for rewriting SPARQL queries using materialized RDF views. The experimental evaluation shows that MARVEL can improve query response time by more than an order of magnitude while effectively handling RDF specifics.

Original language	English
Title of host publication	The Semantic Web – ISWC 2016 : 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part I
Editors	Paul Groth, Elena Simperl, Alasdair Gray, Marta Sabou, Markus Krötzsch, Freddy Lecue, Fabian Flöck, Yolanda Gil
Number of pages	19
Publisher	Springer
Publication date	23 Sept 2016
Pages	341-359
ISBN (Print)	978-3-319-46522-7
ISBN (Electronic)	978-3-319-46523-4
DOIs	https://doi.org/10.1007/978-3-319-46523-4_21
Publication status	Published - 23 Sept 2016
Event	15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016 - Kobe, Japan Duration: 16 Oct 2013 → 20 Oct 2016 Conference number: 15 http://iswc2016.semanticweb.org

Conference

Conference	15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016
Number	15
Country/Territory	Japan
City	Kobe
Period	16/10/2013 → 20/10/2016
Internet address	http://iswc2016.semanticweb.org

Series	Lecture Notes in Computer Science
Volume	9981
ISSN	0302-9743

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10.1007/978-3-319-46523-4_21

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Ibragimov, D., Hose, K., Pedersen, T. B., & Zimányi, E. (2016). Optimizing Aggregate SPARQL Queries Using Materialized RDF Views. In P. Groth, E. Simperl, A. Gray, M. Sabou, M. Krötzsch, F. Lecue, F. Flöck, & Y. Gil (Eds.), The Semantic Web – ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part I (pp. 341-359). Springer. https://doi.org/10.1007/978-3-319-46523-4_21

Ibragimov, Dilshod ; Hose, Katja ; Pedersen, Torben Bach et al. / Optimizing Aggregate SPARQL Queries Using Materialized RDF Views. The Semantic Web – ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part I. editor / Paul Groth ; Elena Simperl ; Alasdair Gray ; Marta Sabou ; Markus Krötzsch ; Freddy Lecue ; Fabian Flöck ; Yolanda Gil. Springer, 2016. pp. 341-359 (Lecture Notes in Computer Science, Vol. 9981).

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title = "Optimizing Aggregate SPARQL Queries Using Materialized RDF Views",

abstract = "During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing techniques. To overcome these limitations, materialized views can be created and used as a source of precomputed partial results during query processing. However, materialized view techniques as proposed for relational databases do not support RDF specifics, such as incompleteness and the need to support implicit (derived) information. To overcome these challenges, this paper proposes MARVEL (MAterialized Rdf Views with Entailment and incompLetness). The approach consists of a view selection algorithm based on an associated RDF-specific cost model, a view definition syntax, and an algorithm for rewriting SPARQL queries using materialized RDF views. The experimental evaluation shows that MARVEL can improve query response time by more than an order of magnitude while effectively handling RDF specifics.",

author = "Dilshod Ibragimov and Katja Hose and Pedersen, {Torben Bach} and Esteban Zim{\'a}nyi",

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booktitle = "The Semantic Web – ISWC 2016",

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Ibragimov, D, Hose, K , Pedersen, TB & Zimányi, E 2016, Optimizing Aggregate SPARQL Queries Using Materialized RDF Views. in P Groth, E Simperl, A Gray, M Sabou, M Krötzsch, F Lecue, F Flöck & Y Gil (eds), The Semantic Web – ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part I. Springer, Lecture Notes in Computer Science, vol. 9981, pp. 341-359, 15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Kobe, Japan, 16/10/2013. https://doi.org/10.1007/978-3-319-46523-4_21

Optimizing Aggregate SPARQL Queries Using Materialized RDF Views. / Ibragimov, Dilshod; Hose, Katja ; Pedersen, Torben Bach et al.
The Semantic Web – ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part I. ed. / Paul Groth; Elena Simperl; Alasdair Gray; Marta Sabou; Markus Krötzsch; Freddy Lecue; Fabian Flöck; Yolanda Gil. Springer, 2016. p. 341-359 (Lecture Notes in Computer Science, Vol. 9981).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Optimizing Aggregate SPARQL Queries Using Materialized RDF Views

AU - Ibragimov, Dilshod

AU - Hose, Katja

AU - Pedersen, Torben Bach

AU - Zimányi, Esteban

N1 - Conference code: 15

PY - 2016/9/23

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N2 - During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing techniques. To overcome these limitations, materialized views can be created and used as a source of precomputed partial results during query processing. However, materialized view techniques as proposed for relational databases do not support RDF specifics, such as incompleteness and the need to support implicit (derived) information. To overcome these challenges, this paper proposes MARVEL (MAterialized Rdf Views with Entailment and incompLetness). The approach consists of a view selection algorithm based on an associated RDF-specific cost model, a view definition syntax, and an algorithm for rewriting SPARQL queries using materialized RDF views. The experimental evaluation shows that MARVEL can improve query response time by more than an order of magnitude while effectively handling RDF specifics.

AB - During recent years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing techniques. To overcome these limitations, materialized views can be created and used as a source of precomputed partial results during query processing. However, materialized view techniques as proposed for relational databases do not support RDF specifics, such as incompleteness and the need to support implicit (derived) information. To overcome these challenges, this paper proposes MARVEL (MAterialized Rdf Views with Entailment and incompLetness). The approach consists of a view selection algorithm based on an associated RDF-specific cost model, a view definition syntax, and an algorithm for rewriting SPARQL queries using materialized RDF views. The experimental evaluation shows that MARVEL can improve query response time by more than an order of magnitude while effectively handling RDF specifics.

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Ibragimov D, Hose K , Pedersen TB, Zimányi E. Optimizing Aggregate SPARQL Queries Using Materialized RDF Views. In Groth P, Simperl E, Gray A, Sabou M, Krötzsch M, Lecue F, Flöck F, Gil Y, editors, The Semantic Web – ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part I. Springer. 2016. p. 341-359. (Lecture Notes in Computer Science, Vol. 9981). doi: 10.1007/978-3-319-46523-4_21

Optimizing Aggregate SPARQL Queries Using Materialized RDF Views

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