Combining Graph Exploration and Fragmentation for Scalable RDF Query Processing-Reference-Cited by-同舟云学术

Combining Graph Exploration and Fragmentation for Scalable RDF Query Processing

Published:2020-03-04 Issue:1 Volume:23 Page:165-183
ISSN:1387-3326
Container-title:Information Systems Frontiers
language:en
Short-container-title:Inf Syst Front

Author:

Khelil Abdallah,Mesmoudi Amin^ORCID,Galicia Jorge,Bellatreche Ladjel,Hacid Mohand-Saïd,Coquery Emmanuel

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Information Systems,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s10796-020-09998-z.pdf

Reference40 articles.

1. Abadi, D.J., Marcus, A., Madden, S.R., Hollenbach, K. (2007). Scalable semantic web data management using vertical partitioning. In Proceedings of the 33rd international conference on very large data bases (pp. 411–422): VLDB Endowment.

2. Aït-Kaci, H., Boyer, R., Lincoln, P , Nasr, R. (1989). Efficient implementation of lattice operations. ACM Transactions on Programming Languages and Systems (TOPLAS), 11(1), 115–146.

3. Al-Harbi, R., Abdelaziz, I., Kalnis, P., Mamoulis, N., Ebrahim, Y., Sahli, M. (2016). Accelerating SPARQL queries by exploiting hash-based locality and adaptive partitioning. VLDB Journal, 25(3), 355–380.

4. Atre, M., Srinivasan, J., Hendler, Bitmat. (2008). Bitmat: a main-memory bit matrix of RDF triples for conjunctive triple pattern queries. In Proceedings of the poster and demonstration session at the 7th international semantic web conference (ISWC2008), Karlsruhe, Germany, October 28.

5. Briggs, M. (2012). Db2 nosql graph store what why & overview.

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