A method of A-BAT algorithm based query optimization for crowd sourcing system

Автор: W.C.Cincy, J.R.Jeba

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 3 vol.10, 2018 года.

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In the field of database administration query optimization is one of the refinement processes. In recent years, huge volumes of data are flooded from different resources, which make query optimization, a difficult task for the researchers. In the crowd sourcing, environment query optimization is the biggest problem. The client is simply required to post an SQL-like subject, and the framework assumes the main issue of organizing the inquiry; execution setup is generated and in the crowd sourcing market places the evaluation plan evaluated. In order to retrieve data fast and reduce query processing time, Query optimization is badly required. In order to optimize the queries, Meta heuristic techniques are used. In this proposed paper, preprocessing method is used to mine the information from the Crowd. The Original population based ABC algorithm has low convergence speed. In this paper a novel A-BAT algorithm is proposed, which highly improve convergence speed, accuracy and Latency. This algorithm uses a Random walk phase. The proposed algorithm had better optimization accuracy, convergence rate, and robustness.

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Crowdsourcing, Query Optimization, ABC Algorithm, BAT Algorithm, Preprocessing, A-BAT Algorithm

Короткий адрес: https://sciup.org/15016469

IDR: 15016469   |   DOI: 10.5815/ijisa.2018.03.04

Список литературы A method of A-BAT algorithm based query optimization for crowd sourcing system

  • J. Fan, M. Zhang, S. Kok, M. Lu, and B.C. Ooi, “Crowdop: Query Optimization for Declarative Crowdsourcing Systems,” IEEE Trans. Knowl. Data Eng, Vol. 27, No. 8, Aug 2015.
  • S. Wu, X. Wang, S. Wang, Z. Zhang, and A. Tung, “K-Anonymity for Crowdsourcing Database,” IEEE Trans. Knowl. Data Eng, Vol. 26, No. 9, Sep 2014.
  • Trushkowsky, T. Kraska, M. Franklin, P. Sarkar, and V. Ramachandran, “Crowdsourcing Enumeration Queries: Estimators and Interfaces,” IEEE Trans. Knowl. Data Eng, Vol.27 , No. 7, July 2015.
  • E. Ciceri, P. Fraternali, D. Martinenghi, and M.Tagliasacchi, “Crowdsourcing for Top-K Query Processing over Uncertain Data,” IEEE Trans. Knowl. Data Eng, Vol. 27, No. 2, Aug 2015.
  • Archana, P. Srinivasan, “Efficient query optimization for Easy Retrieval of Crowd Resources,” International Journal of Innovative Research in Computer and Communication Engineering, Vol.4, Issue 2, Feb 2016.
  • A. S. Patil, A. D. Katiyar, S. A. Singh, P. P. Kachhava, and D. B. Bagul,”Crowd Search: Generic Crowdsourcing Systems Using Query Optimization, “International Journal On Recent and Innovation Trends in Computing and Communication, Vol.3, Issue 9, Seb 2015.
  • M. K. Chandrakant, B. S. Sukhadeo,”A Implementation On Forecasting Behavioral Outcomes through Crowdsourcing Mechanism,” International Journal of Innovative Research in Computer and Communication Engineering, Vol.3, Issue 6, June 2015.
  • T. Ali, E. S. Nasr, “CrowdCE: A Collaboration Model for Crowdsourcing Software with Computing Elements,” International Journal of Innovative Research in Computer and Communication Engineering, Vol.4, Issue 2, Feb 2016.
  • C. Nieke, U. Guntzer, W. Balke,” Top Crowd-enabled Top-K Retrieval on Incomplete Data,” Springer, 2014.
  • S. Uma, J. Sugua,” Human Interaction Pattern Mining Using Enhanced Artificial Bee Colony Algorithm,” International Journal of Innovative Research in Computer and Communication Engineering,Vol.3,Issue 9,Sep 2015.
  • Priya I. Borgar, Leena H. Patil,” A Model of Hybrid Genetic Algorithm-Particle Swarm Optimization(HGAPSO)Based Query Optimization for Web Information Retrieval,” International journal of Research in Engineering and Technology,Vol.2, Issue 01, Jan 2013.
  • P. R. Ruphashii, R. Gomati,” Single Query Optimization of SPARQL Using Ant Colony Optimization,” International Journal of Innovative Research in Computer and Communication Engineering, Vol.2, Special Issue 1, Mar 2014.
  • S. Babaeizadeh, R. Ahmad,” An Efficient Artificial Bee Colony Algorithm for constrained Optimization Problems,” Journal of Engineering and Applied Sciences, Medwell Journals, 2014.
  • Y. Xu, P. Fan, L. Yuan, “A Simple and Efficient Bee Colony Algorithm, “Mathematical Problems in Engineering, 2013.
  • S. Kumar, V.K. Sharma, R. Kumari,”An Improved Memetic Search in Artificial Bee Colony Algorithm, “International Journal of Computer Science and Information Techologies, Vol.5, Issue 2, 2014.
  • J.R. Jeba, S.P. Victor, “A Novel approach for finding Frequent Item Sets with Hybrid Strategies, “International Journal Of Computer Applications,Vol 17, No.5, 2011.
  • J.R. Jeba, S.P. Victor, “Comparison of frequent item set mining algorithms, “International Journal of Computer Applications, Vol.2, No 6, 2011.
  • D.S. Mispha, J.R. Jeba, “Scheduling Effective Cloud Updates in Streaming Data Warehouses using RECSS Algorithm,” International Journal of Applied Engineering Research, Vol. 11, No. 5, 2016.
  • Jincy, J.R. Jeba, “Survey on web content extraction,” International Journal of Applied Engineering Research, Vol. 11, No .7, 2016.
  • Induja, V.P. Eswaramurthy,” Bat Algorithm: An Overview and its Applications, “International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 1, January 2016
  • X. S. Yang, “A new meta heuristic bat-inspired algorithm,” Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), vol. 284, (2010), pp. 65-74.
  • X. S. Yang, “Bat algorithm: literature review and applications,” International Journal of Bio-Inspired Computation, vol. 5, no. 3, (2013), pp. 141-149.
  • D. Karaboga and B. Basturk,” On the performance of artificial bee colony (abc) algorithm,” Applied Soft Computing, 8(1):687–697, 2008.
  • D. Karaboga, “An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Report-TR06,” Computer Engineering Department, 2005.
  • J. Howe,” The Rise of Crowdsourcing, “Wired, 2006.
  • M.Faiza Abdul Salam, and Azurliza Abu, “A cluster-based Deviation Detection Task Using the Artificial Bee Colony Algorithm,” International journal of soft computing,” 7(2):71-78, 2012.
  • Shelja Singla, Priyanka Jarial, Gaurav Mittal, “Hybridization of Cuckoo Search & Artificial bee Colony Optimization for Satellite Image Classification,” International Journal of Advanced Research in Computer and Communication Engineering,” Vol. 4, Issue 6, June 2015.
  • Sridhar Mandapati, Raveendra Babu Bhogapathi, Ratna Babu Chekka,"A Hybrid Algorithm for Privacy Preserving in Data Mining," International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.8, pp. .47-53, 2013. DOI: .5815/ijisa.2013.08.06.
  • Osama Abdel-Raouf, Mohamed Abdel-Baset, Ibrahim El-henawy,"An Improved Chaotic Bat Algorithm for Solving Integer Programming Problems", IJMECS, vol.6, no.8, pp.18-24, 2014.DOI: 10.5815/ijmecs.2014.08.03.
  • A. J. Umbarkar, M. S. Joshi, P. D. Sheth," OpenMP Dual Population Genetic Algorithm for Solving Constrained Optimization Problems", IJIEEB, vol.7, no.1, pp.59-65, 2015. DOI: 10.5815/ijieeb.2015.01.08
  • Cincy.W.C, J.R.Jeba. “Survey on Query Optimization for Declarative Crowdsourcing Systems,” Int. J. Control Theory Appl., vol. 10, no. 27, pp. 77–82, 2017.
  • Cincy.W.C,J.R.Jeba “Query Processing in the Crowdsourcing Environment,” Int. J. Sci. Res. Sci. Technol., vol. 3, no. 7, pp. 1037–1041, 2017.
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