Prime Learning – Ant Colony Optimization Technique for Query Optimization in Distributed Database System

Prime Learning – Ant Colony Optimization Technique for Query Optimization in Distributed Database System
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : Praveena Mydolalu Veerappa, Ajeet Annarao Chikkamannur
DOI : 10.14445/22315381/IJETT-V70I8P216

How to Cite?

Praveena Mydolalu Veerappa, Ajeet Annarao Chikkamannur, "Prime Learning – Ant Colony Optimization Technique for Query Optimization in Distributed Database System," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 158-165, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P216

Abstract
Query optimization is an important factor in a distributed database system that finds the parameters for the optimal execution of a plan to reduce the runtime. Query optimization is a challenging task in a distributed system that helps improve the database's efficiency. Various pieces of research applied Ant Colony Optimization (ACO) based method to improve query optimisation performance. The Prime Learning - Ant Colony Optimization (PL-ACO) method is applied to increase query optimisation performance in this research. The Prime strategy method is applied in the ACO method to replace the worst learner with the best learner to improve the learning rate. The key idea of the proposed method is to use the Prime strategy on Ant Colony Optimization to reduce the search space-time for query joins in a distributed database system. For Query access, processing and resource processing cost, the fitness function is created for each possible query join solution. For fitter functions, queries are being taken for processing in the next round, and weaker ants are eliminated. This process reduces the number of ants at each iteration, and the optimal solution is achieved quicker. Various queries in the database were used to test the efficiency of PL-ACO in query optimization. The system model is developed to apply the query in the system and evaluate the cost of the model. The proposed PL-ACO method has 10 iterations for 0.2 cost and existing ACO method has 68 iterations, and the ACO-Genetic Algorithm (GA) method has 65 iterations.

Keywords
Genetic Algorithm, Learning Rate, Prime Strategy, Query optimization.

Reference
[1] R. Sahal, M.H. Khafagy, F.A. Omara, “Exploiting Coarse-Grained Reused-Based Opportunities in Big Data Multi-Query Optimization,” Journal of Computational Science, vol. 26, pp. 432-452, 2018.
[2] M. Renukadevi, E.M. Anita, D. Mohana Geetha, “An Efficient Privacy‐Preserving Model Based on OMFTSA for Query Optimization in Crowdsourcing,” Concurrency and Computation: Practice and Experience, vol. 33, pp. e6447, 2021.
[3] Y. Jiang, K. Zhang, Y. Qian, L. Zhou, “Reinforcement Learning based Query Optimization in Differentially Private IoT Data Publishing,” IEEE Internet of Things Journal, 2021.
[4] P. Michiardi, D. Carra, S. Migliorini, “Cache-based Multi-Query Optimization for Data-Intensive Scalable Computing Frameworks,” Information Systems Frontiers, vol. 23, pp. 35-51, 2021.
[5] M. Sharma, G. Singh, R. Singh, “A Review of Different Cost-Based Distributed Query Optimizers,” Progress in Artificial Intelligence, vol. 8, pp. 45-62, 2019.
[6] L. Chen, Y. Lin, J. Wang, H. Huang, D. Chen, Y. Wu, “Query Grouping–Based Multi‐Query Optimization Framework for Interactive SQL Query Engines on Hadoop,” Concurrency and Computation: Practice and Experience, vol. 30, pp. e4676, 2018.
[7] A. Sebaa, A. Tari, “Query Optimization in Cloud Environments: Challenges, Taxonomy, and Techniques,” The Journal of Supercomputing, vol. 75, pp. 5420-5450, 2019.
[8] Z.B. Ozger, N.Y. Uslu, “An Effective Discrete Artificial Bee Colony Based SPARQL Query Path Optimization by Reordering Triples,” Journal of Computer Science and Technology, vol. 36, pp. 445-462, 2021.
[9] E. Mella, M.A. Rodríguez, L. Bravo, D. Gatica, “Query Rewriting for Semantic Query Optimization in Spatial Databases,” Geoinformatica, vol. 23, pp. 79-104, 2019.
[10] J. Kossmann, T. Papenbrock, F. Naumann, “Data Dependencies for Query Optimization: A Survey,” The VLDB Journal, pp. 1-22, 2021.
[11] V. Panahi, N.J. Navimipour, “Join Query Optimization in the Distributed Database System using an Artificial Bee Colony Algorithm and Genetic Operators,” Concurrency and Computation: Practice and Experience, vol. 31, pp. e5218, 2019.
[12] M. Jafarinejad, and M. Amini, “Multi-Join Query Optimization in Bucket-Based Encrypted Databases using an Enhanced Ant Colony Optimization Algorithm,” Distributed and Parallel Databases, vol. 36, pp. 399-441, 2018.
[13] S.A. Mohsin, S.M. Darwish, A. Younes, “QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database,” IEEE Access, vol. 9, pp. 15833-15846, 2021.
[14] L. G. Igakeh, V. I. E. Anireh, D. Matthias, "Implementation of Ant Colony Optimization For Call Drop In Gsm Network," SSRG International Journal of Computer Science and Engineering, vol. 7, no. 2, pp. 57-67, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I2P107
[15] D. Kumar, and V.K. Jha, “An Improved Query Optimization Process in Big Data Using ACO-GA Algorithm and HDFS Map Reduce Technique,” Distributed and Parallel Databases, vol. 39, pp. 79-96, 2021.
[16] S.A. Mohsin, A. Younes, S.M. Darwish, “Dynamic Cost Ant Colony Algorithm to Optimize Query for Distributed Database Based on Quantum-Inspired Approach,” Symmetry, vol. 13, no. 1, pp. 70, 2021.
[17] B. Zheng, X. Li, Z. Tian, L. Meng, “Optimization Method for Distributed Database Query Based on An Adaptive Double Entropy Genetic Algorithm,” IEEE Access, vol. 10, pp. 4640–4648, 2022.
[18] W. Deng, J. Xu, and H. Zhao, “An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem,” IEEE Access, vol. 7, pp. 20281-20292, 2019.
[19] J. Uthayakumar, N. Metawa, K. Shankar, S.K. Lakshmanaprabu, “Financial Crisis Prediction Model Using Ant Colony Optimization,” International Journal of Information Management, vol. 50, pp. 538-556, 2020.
[20] M. Paniri, M.B. Dowlatshahi, H. Nezamabadi-pour, “MLACO: A Multi-Label Feature Selection Algorithm Based on Ant Colony Optimization,” Knowledge-Based Systems, vol. 192, pp. 105285, 2020.
[21] Y. Li, H. Soleimani, M. Zohal, “An Improved Ant Colony Optimization Algorithm for the Multi-Depot Green Vehicle Routing Problem with Multiple Objectives,” Journal of Cleaner Production, vol. 227, pp. 1161-1172, 2019.
[22] C. Xu, B. Gordan, M. Koopialipoor, D.J. Armaghani, M.M. Tahir, X. Zhang, “Improving Performance of Retaining Walls Under Dynamic Conditions Developing an Optimized ANN Based on Ant Colony Optimization Technique,” IEEE Access, vol. 7, pp. 94692- 94700, 2019.
[23] M.A. Ariffin, R. Ibrahim, I.S. Ibrahim, J.A. Wahab, “Test Cases Prioritization Using Ant Colony Optimization and Firefly Algorithm,” International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 22-28, 2022.
[24] N Bhushana Babu D, E V Krishna Rao, K.S.N. Murthy, “Inter-Gateway Handoff Management Using Ant Colony Optimization (ACO) for Wireless Mesh Networks,” International Journal of Engineering Trends and Technology, vol. 68, no. 11, pp. 63-71, 2020.
[25] X.Y. Gao, R. Sahal, G.X. Chen, M.H. Khafagy, F.A. Omara, “Exploiting Sharing Join Opportunities in Big Data Multiquery Optimization with Flink,” Complexity, 2020.