Exploring the Synergy between Query Optimization
and Parallel Processing for Efficient Database
Management
Authors: Ankur Bhardwaj, India
Parallel processing and query optimization are two crucial techniques used in the field of database management. Parallel processing involves dividing a large task into smaller sub-tasks executed simultaneously on multiple processors, while query optimization aims to improve the performance of a database system by finding the most efficient way to execute a query. These techniques are closely related, and their combination can significantly reduce the time required to process complex queries on large datasets. Applications of query processing include business intelligence, e-commerce, healthcare, social media, and finance, while parallel processing is widely used in scientific simulations, image and video processing, machine learning, data analytics, and financial modeling. The use of parallel processing for query processing involves query optimization, query partitioning, sub-query execution, and result merging. Efficient and effective parallel processing and query optimization are essential for managing and analyzing large and complex datasets, and their importance will only continue to increase as data continues to grow..
Keywords: EParallel processing, Query optimization, Database management, Sub-tasks, Multiple
processors, Performance improvement, Efficient query execution;
References
1. Moldovan, Dan I. Parallel processing from applications to systems. Elsevier, 2014.
2. Sharma, Manik, Gurdev Singh, and Harsimran Kaur. "A study of BNP parallel task scheduling algorithms metric's for distributed database system." International Journal of Distributed and Parallel Systems 3.1 (2012): 157.
3. Ioannidis, Yannis E. "Query optimization." ACM Computing Surveys (CSUR) 28.1 (1996): 121-123.
4. Sharma, Manik, et al. "Stochastic Analysis of DSS Queries for a Distributed Database Design." International Journal of Computer Applications 83.5 (2013): 36-42.
5. Sharma, Manik, Gurdev Singh, and Rajinder Virk. "Analysis of Joins and Semi Joins in a Distributed Database Queries." International Journal of Computer Applications 49.16 (2012).
6. Yu, Clement T., and Weiyi Meng. Principles of database query processing for advanced applications. Morgan Kaufmann Publishers Inc., 1998.
7. Kossmann, Donald. "The state of the art in distributed query processing." ACM Computing Surveys (CSUR) 32.4 (2000): 422-469.
8. Sharma, Manik, et al. "Design and analysis of stochastic query optimizer for biobank databases." 2015 15th International Conference on Computational Science and Its Applications. IEEE, 2015.
9. Sharma, M., G. Singh, and R. Singh. "Clinical decision support system query optimizer using hybrid Firefly and controlled Genetic Algorithm." Journal of King Saud University-Computer and Information Sciences 33.7 (2021): 798-809.
10. Virmani, Charu, Dimple Juneja, and Anuradha Pillai. "Design of query processing system to retrieve information from social network using NLP." KSII Transactions on Internet and Information Systems (TIIS) 12.3 (2018): 1168-1188.
11. Sharma, Manik, et al. "Stochastic Analysis of DSS Queries for a Distributed Database Design." International Journal of Computer Applications 83.5 (2013): 36-42.
12. ADELI, HOJJAT, and PRASAD VISHNUBHOTLA. "Parallel processing." Computer-Aided Civil and Infrastructure Engineering 2.3 (1987): 257-269.
13. Roosta, Seyed H. Parallel processing and parallel algorithms: theory and computation. Springer Science & Business Media, 2012.
14. Ganguly, Sumit, Waqar Hasan, and Ravi Krishnamurthy. "Query optimization for parallel execution." Proceedings of the 1992 ACM SIGMOD international conference on management of data. 1992.
15. Wu, Sai, et al. "Query optimization for massively parallel data processing." Proceedings of the 2nd ACM Symposium on Cloud Computing. 2011.
