Design and Development of Great-Recital Processing Created Methodology for Refining Semantic-Created Amalgamated Data Dispensation Technology

Authors

  • S. Ravichandran Professor, Department of Computer Science, Annai Fathima College of Arts and Science, Madurai, Tamil Nadu, India

Keywords:

SPARQL, CUDA, Linked Open Data, Parallelization, HPC, Semantic Web, Linked Data

Abstract

Recovering RDF datasets from appropriated information sources has become a fundamental interaction to accomplish the vision of the semantic web. The semantic web vision advances an everyday growing of the semantic diagram that requires some preparing upgrades including SPARQL end-point and combined questions to secure the interminable extension. The current progressed elite figuring engineering has been growing quickly to conquer numerous issues including execution. Elite registering can possibly be utilized to improve the unified SPARQL inquiry and generally speaking activity including the performance and handling. Hence, this work surveys the procedures to advance the improvement on isolated and distant combined semantic-based information. That is, the superior figuring climate including equipment engineering and extraordinary design registering has been overviewed. Besides, working semantic diagram combination necessities is dissected. Besides, the current strategies to demonstrate SPARQL execution are considered. Furthermore, an examination about the usage of cutting edge equipment figuring engineering is investigated to upgrade the execution of the current SPARQL alliance administration activity.

References

T. Berners-Lee, J. Hendler, and O. Lassila, “The Semantic Web,” Scientific American, 2001.

G. Antoniou, P. Groth, F.Van Harmelen, R. Hoekstra and A Semantic Web Primer, Third ed., MIT Press, 2012.

C. Weiss, P. Karras, and A. Bernstein, “Hexastore: Sextup leindexing for semantic web data management,” Proc. VLDB Endow., Vol. 2, No. 5, pp. 45-55, 2008.

S. Schlobach and C. A. Knoblock,” Dealing with the messiness of the web of data,” Journal of Web Semantics, Vol. 4, No. 3, pp. 109-119, 2012.

X. Li, Z. Niu, and C. Zhang,” Towards efficient distributed SPARQL queries on linked data,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bio informatics), 2012.

A. I. Torre-Bastida, E. Villar Rodriguez, M. N. Bilbao and J. DelSer, “Intelligent SPARQL end points: Optimizing execution performance by auto maticquery relaxation and queue scheduling,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016.

M. Acosta, M. E. Vidal, T. Lampo, J. Castillo and E. Ruckhaus, “ANAP- SID: An adaptive query processing engine for SPARQL end points,” in Lecture Notes in Computer Science (including subseries Lecture Notes In Artificial Intelligence and Lecture Notes in Bioinformatics), 2011.

M. Saleem and A. C. Ngonga Ngomo, “HiBISCuS: Hyper graph-based source selection for SPARQL end point federation,” in Lecture Notes in Computer Science (including sub series Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3, No. 6, pp. 90-102, 2014.

B. Quilitz and U. Leser, “Querying distributed RDF data sources with SPARQL,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2, No. 8, pp. 214-225, 2008.

M. Saleem and A. C. Ngonga Ngomo, “HiBISCuS: Hyper graph based Source selection for SPARQL end point federation,” in Lecture Notes In Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014.

A. Bernstein, C. Kiefer and M. Stocker, “Opt ARQ: ASPARQL Optimization Approach based on Triple Pattern Selectivity Estimation,” Univ. Zurich, Vol. 3, No. 1, pp. 56-66, Dep., 2007.

O. Hartig, “SQUIN: A Traversal Based Query Execution System for the Web of Linked Data,” in Proceedings of the 2013 International conference on Management of data – SIGMOD, Vol. 13, No. 7, pp. 108-120, 2013.

J. Sathiamoorthy, Dr. B. Ramakrishnan and M. Usha “A Trusted Waterfall Frame work Based Peer to Peer Protocol for Reliable and Energy Efficient Data Transmission in MANETs,” Springer Journal of Wireless Personal Communication, Article First Online: Vol. 3, No. 4, pp. 78-89, 17 May 2018.

D. Bednarek, J. Dokulil, J. Yahoo and F. Zavoral, “Using Methods of Parallel Semi-structured Data Processing for Semantic Web,” in Third International Conference on Advances in Semantic Processing, Vol. 6, No. 7, pp. 44-49, 2009.

J. Sathiamoorthy, and Dr. B. Ramakrishna “A competent three tier fuzzy cluster algorithm for enhanced data transmission in cluster EAACK MANETs,” Springer Soft Computing, Vol. 8, No. 6, pp. 123-135, July 2017.

X. Wang, T. Tiropanis, and H.C. Davis, “LHD: Optimising linked data query processing using parallelisation,” in CEUR Work shop Proceed, Vol. 5, No. 3, pp. 71-82, 2013.

M. Hajibaba and S. Gorgin “A Review on Modern Distributed Computing Paradigms: Cloud Computing, Jungle Computing and Fog Computing,” J. Compute. Inf. Tech., Vol. 22, No. 2, pp. 69-84, 2014.

Downloads

Published

15-05-2021

How to Cite

Ravichandran, S. (2021). Design and Development of Great-Recital Processing Created Methodology for Refining Semantic-Created Amalgamated Data Dispensation Technology. Asian Journal of Science and Applied Technology, 10(1), 13–19. Retrieved from https://ojs.trp.org.in/index.php/ajsat/article/view/2811