The enormous increase in the amount of web pages day by day leads to progress in semantic web data management.
The issues in semantic web data management are increasing and there is a need for improvement in research to handle them.
One of the most important issues is the process of query optimization. The semantic web data stored in the form of Resource
Description Framework (RDF) data can be queried using the popular query language SPARQL Protocol And RDF Query
Language (SPARQL). As the size of the data increases, complication arises in querying the RDF data. The problem of
querying the RDF graphs involves multiple join operations and optimizing those joins becomes NP-hard. Nature inspired
algorithms are becoming much popular in recent days to handle problems with high complexity. In this research, a hybrid BAT
Algorithm with Cuckoo Search (BATCS) is proposed to handle the problem of query optimization. The algorithm applies the
echolocation behaviour of bats and hybrids with cuckoo search if the best solution stagnates for a designated number of
iterations. Experiments were conducted with benchmark data sets and the algorithm proves that it performs efficiently in terms
of query execution time.
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[2] Gomathi R. and Sharmila D., A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation, The Scientific World Journal, vol. 2014, pp.1-7, 2014.
[3] Hogenboom A., Milea V., Frasincar F., and Kaymak U., RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms, in Proceedings of International Conference on Electronic Commerce and Web Technologies, Linz, pp. 181-192, 2009.
[4] Lynden S., Kojima I., Matono A., Nakamura A., and Yui M., A Hybrid Approach to Linked Data Query Processing with Time Constraints, in Proceeding of LDOW 996, Rio de Janeiro, 2013.
[5] Meiyappan Y. and Iyengar S., Interactive Query Expansion using Concept-Based Directions Finder Based on Wikipedia, The International Arab Journal of Information Technology, vol. 10, no. 6, pp. 571-578, 2013.
[6] Schmidt M., Meier M., and Lausen G., Foundations of SPARQL Query Optimization, in Proceedings of the 13th International Conference on Database Theory, Lausanne, pp. 4- 33, 2010.
[7] Sinha M. and Chande S., Query Optimization Using Genetic Algorithms, Research Journal of Information Technology, vol. 2, no. 3, pp. 139- 144, 2010.
[8] Steinbrun M., Moerkotte G., and Kemper A., Heuristic and Randomized Optimization for the Join Ordering Problem, VLDB Journal, vol. 6, no. 3, pp. 191-208, 1997.
[9] Yang X. and Deb S., Cuckoo Search Via Levy Flights, in Proceedings of the World Congress on Nature and Biologically Inspired Computing, Coimbatore, pp. 210-214, 2009.
[10] Yang X. and He X., Bat Algorithm: Literature Review and Applications, International Journal of Bio-Inspired Computation, vol. 5, no. 3, pp. 141-149, 2013.
[11] Yu J., Zhang L., Chen M., and Liu X., Hybrid Ant Algorithm Based Query Processing with Multiagents in Sensor Networks, International Journal of Distributed Sensor Networks, vol. 9, no. 9, pp. 1-7, 2013.
[12] Yunus M., Zainuddin R., and Abdullah N., Semantic Method for Query Translation, The International Arab Journal of Information Technology, vol. 10, no. 3, pp. 253-259, 2013.
[13] Zhou Z., Using Heuristics and Genetic Algorithms for Large-scale Database Query Optimization, Journal of Information and Computing Science, vol. 2, no. 4, pp. 261-280, 2007. Gomathi Ramalingam completed her under graduation in the year 2003 and post graduation in the year 2011. She is pursuing her Doctorate in Anna University, Chennai. At present she is working as an Assistant Professor (Sr.Grade) in the Department of Computer Science and Engineering at Bannari Amman Institute of Technology, Sathyamangalam, Erode Dt. She has over 12 years of teaching experience. She has published her papers in 4 International conferences, 6 National Conferences and 8 International Journals. Sharmila Dhandapani completed her under graduation in the year 1996 and post graduation in the year 2004. She has been awarded Doctorate in the year 2010 from Anna University, Chennai. At present she is working as Professor and Head of Electronics and Instrumentation Engineering in Bannari Amman Institute of Technology, Sathyamangalam. She has over 18 years of teaching experience. She has published her papers in 2 National and 35 International Journals. She has also presented her papers in 10 National and 19 International Conferences.
Cite this
1Department of Computer Science and Engineering, Bannari Amman Institute of Technology, India 2Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology,, "A Hybrid BATCS Algorithm to Generate Optimal", The International Arab Journal of Information Technology (IAJIT) ,Volume 15, Number 03, pp. 1 - 7, May 2018, doi: .
@ARTICLE{3716,
author={1Department of Computer Science and Engineering, Bannari Amman Institute of Technology, India 2Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology,},
journal={The International Arab Journal of Information Technology (IAJIT)},
title={A Hybrid BATCS Algorithm to Generate Optimal},
volume={15},
number={03},
pages={1 - 7},
doi={},
year={1970}
}
TY - JOUR
TI - A Hybrid BATCS Algorithm to Generate Optimal
T2 -
SP - 1
EP - 7
AU - 1Department of Computer Science and Engineering
AU - Bannari Amman Institute of Technology
AU - India 2Department of Electronics and Instrumentation Engineering
AU - Bannari Amman Institute of Technology
AU -
DO -
JO - The International Arab Journal of Information Technology (IAJIT)
IS - 9
SN - 2413-9351
VO - 15
VL - 15
JA -
Y1 - Jan 1970
ER -
PY - 1970
1Department of Computer Science and Engineering, Bannari Amman Institute of Technology, India 2Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology,, " A Hybrid BATCS Algorithm to Generate Optimal", The International Arab Journal of Information Technology (IAJIT) ,Volume 15, Number 03, pp. 1 - 7, May 2018, doi: .
Abstract: The enormous increase in the amount of web pages day by day leads to progress in semantic web data management.
The issues in semantic web data management are increasing and there is a need for improvement in research to handle them.
One of the most important issues is the process of query optimization. The semantic web data stored in the form of Resource
Description Framework (RDF) data can be queried using the popular query language SPARQL Protocol And RDF Query
Language (SPARQL). As the size of the data increases, complication arises in querying the RDF data. The problem of
querying the RDF graphs involves multiple join operations and optimizing those joins becomes NP-hard. Nature inspired
algorithms are becoming much popular in recent days to handle problems with high complexity. In this research, a hybrid BAT
Algorithm with Cuckoo Search (BATCS) is proposed to handle the problem of query optimization. The algorithm applies the
echolocation behaviour of bats and hybrids with cuckoo search if the best solution stagnates for a designated number of
iterations. Experiments were conducted with benchmark data sets and the algorithm proves that it performs efficiently in terms
of query execution time. URL: https://iajit.org/paper/3716
@ARTICLE{3716,
author={1Department of Computer Science and Engineering, Bannari Amman Institute of Technology, India 2Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology,},
journal={The International Arab Journal of Information Technology (IAJIT)},
title={A Hybrid BATCS Algorithm to Generate Optimal},
volume={15},
number={03},
pages={1 - 7},
doi={},
year={1970}
,abstract={The enormous increase in the amount of web pages day by day leads to progress in semantic web data management.
The issues in semantic web data management are increasing and there is a need for improvement in research to handle them.
One of the most important issues is the process of query optimization. The semantic web data stored in the form of Resource
Description Framework (RDF) data can be queried using the popular query language SPARQL Protocol And RDF Query
Language (SPARQL). As the size of the data increases, complication arises in querying the RDF data. The problem of
querying the RDF graphs involves multiple join operations and optimizing those joins becomes NP-hard. Nature inspired
algorithms are becoming much popular in recent days to handle problems with high complexity. In this research, a hybrid BAT
Algorithm with Cuckoo Search (BATCS) is proposed to handle the problem of query optimization. The algorithm applies the
echolocation behaviour of bats and hybrids with cuckoo search if the best solution stagnates for a designated number of
iterations. Experiments were conducted with benchmark data sets and the algorithm proves that it performs efficiently in terms
of query execution time.},
keywords={Data management, query optimization, nature inspired algorithms, bat algorithm, cuckoo search algorithm},
ISSN={2413-9351},
month={Jan}}
TY - JOUR
TI - A Hybrid BATCS Algorithm to Generate Optimal
T2 -
SP - 1
EP - 7
AU - 1Department of Computer Science and Engineering
AU - Bannari Amman Institute of Technology
AU - India 2Department of Electronics and Instrumentation Engineering
AU - Bannari Amman Institute of Technology
AU -
DO -
JO - The International Arab Journal of Information Technology (IAJIT)
IS - 9
SN - 2413-9351
VO - 15
VL - 15
JA -
Y1 - Jan 1970
ER -
PY - 1970
AB - The enormous increase in the amount of web pages day by day leads to progress in semantic web data management.
The issues in semantic web data management are increasing and there is a need for improvement in research to handle them.
One of the most important issues is the process of query optimization. The semantic web data stored in the form of Resource
Description Framework (RDF) data can be queried using the popular query language SPARQL Protocol And RDF Query
Language (SPARQL). As the size of the data increases, complication arises in querying the RDF data. The problem of
querying the RDF graphs involves multiple join operations and optimizing those joins becomes NP-hard. Nature inspired
algorithms are becoming much popular in recent days to handle problems with high complexity. In this research, a hybrid BAT
Algorithm with Cuckoo Search (BATCS) is proposed to handle the problem of query optimization. The algorithm applies the
echolocation behaviour of bats and hybrids with cuckoo search if the best solution stagnates for a designated number of
iterations. Experiments were conducted with benchmark data sets and the algorithm proves that it performs efficiently in terms
of query execution time.