External Sorting for Index Construction of Large Semantic Web Databases

Sven Groppe; Jinghua Groppe

doi:10.1145/1774088.1774382

External Sorting for Index Construction of Large Semantic Web Databases

Institute of Information Systems

Abstract

Today's Semantic Web datasets become increasingly larger containing up to several hundred million triples. The performance of index construction is a crucial factor for the success of large Semantic Web databases. In this paper, we propose two new approaches for RDF index construction: External chunks merge sort and Distribution Sort for RDF. The former stores and retrieves chunks from a special chunks heap to speed up replacement selection. The latter leverages the RDF-specific properties to construct RDF indices and significantly improves the performance. Our experimental results show that our approaches significantly speed up RDF index construction, and are important techniques for large Semantic Web databases.

Original language	English
Title of host publication	Proceedings of the 2010 ACM Symposium on Applied Computing
Number of pages	8
Place of Publication	New York, NY, USA
Publisher	ACM
Publication date	22.03.2010
Pages	1373-1380
ISBN (Print)	978-1-60558-639-7
DOIs	https://doi.org/10.1145/1774088.1774382
Publication status	Published - 22.03.2010
Event	25th Annual ACM Symposium on Applied Computing - Sierre, Switzerland Duration: 22.03.2010 → 26.03.2010 Conference number: 81090

Research Areas and Centers

Research Area: Intelligent Systems
Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

4.43-03 Security and Dependability, Operating, Communication and Distributed Systems

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/1774088.1774382

Cite this

@inproceedings{5da26b4c9de94ce988916d529b607a60,

title = "External Sorting for Index Construction of Large Semantic Web Databases",

abstract = "Today's Semantic Web datasets become increasingly larger containing up to several hundred million triples. The performance of index construction is a crucial factor for the success of large Semantic Web databases. In this paper, we propose two new approaches for RDF index construction: External chunks merge sort and Distribution Sort for RDF. The former stores and retrieves chunks from a special chunks heap to speed up replacement selection. The latter leverages the RDF-specific properties to construct RDF indices and significantly improves the performance. Our experimental results show that our approaches significantly speed up RDF index construction, and are important techniques for large Semantic Web databases.",

author = "Sven Groppe and Jinghua Groppe",

year = "2010",

month = mar,

day = "22",

doi = "10.1145/1774088.1774382",

language = "English",

isbn = "978-1-60558-639-7",

series = "Proceedings of the ACM Symposium on Applied Computing",

publisher = "ACM",

pages = "1373--1380",

booktitle = "Proceedings of the 2010 ACM Symposium on Applied Computing",

note = "25th Annual ACM Symposium on Applied Computing , SAC 2010 ; Conference date: 22-03-2010 Through 26-03-2010",

}

Groppe, S & Groppe, J 2010, External Sorting for Index Construction of Large Semantic Web Databases. in Proceedings of the 2010 ACM Symposium on Applied Computing. Proceedings of the ACM Symposium on Applied Computing, ACM, New York, NY, USA, pp. 1373-1380, 25th Annual ACM Symposium on Applied Computing , Sierre, Switzerland, 22.03.10. https://doi.org/10.1145/1774088.1774382

External Sorting for Index Construction of Large Semantic Web Databases. / Groppe, Sven ; Groppe, Jinghua.
Proceedings of the 2010 ACM Symposium on Applied Computing. New York, NY, USA: ACM, 2010. p. 1373-1380 (Proceedings of the ACM Symposium on Applied Computing).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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AU - Groppe, Jinghua

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N2 - Today's Semantic Web datasets become increasingly larger containing up to several hundred million triples. The performance of index construction is a crucial factor for the success of large Semantic Web databases. In this paper, we propose two new approaches for RDF index construction: External chunks merge sort and Distribution Sort for RDF. The former stores and retrieves chunks from a special chunks heap to speed up replacement selection. The latter leverages the RDF-specific properties to construct RDF indices and significantly improves the performance. Our experimental results show that our approaches significantly speed up RDF index construction, and are important techniques for large Semantic Web databases.

AB - Today's Semantic Web datasets become increasingly larger containing up to several hundred million triples. The performance of index construction is a crucial factor for the success of large Semantic Web databases. In this paper, we propose two new approaches for RDF index construction: External chunks merge sort and Distribution Sort for RDF. The former stores and retrieves chunks from a special chunks heap to speed up replacement selection. The latter leverages the RDF-specific properties to construct RDF indices and significantly improves the performance. Our experimental results show that our approaches significantly speed up RDF index construction, and are important techniques for large Semantic Web databases.

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M3 - Conference contribution

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T3 - Proceedings of the ACM Symposium on Applied Computing

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T2 - 25th Annual ACM Symposium on Applied Computing

Y2 - 22 March 2010 through 26 March 2010

ER -

External Sorting for Index Construction of Large Semantic Web Databases

Abstract

Research Areas and Centers

DFG Research Classification Scheme

UN SDGs

Access to Document

Other files and links

Fingerprint

Logically and physically optimized Semantic Web database engine

Cite this

External Sorting for Index Construction of Large Semantic Web Databases

Abstract

Research Areas and Centers

DFG Research Classification Scheme

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

Logically and physically optimized Semantic Web database engine

Cite this