Article: AltWOA: enhancing query performance with clustering-based optimisation Journal: International Journal of Cloud Computing (IJCC) 2024 Vol.13 No.6 pp.581 - 604 Abstract: Big data (BD) is gaining a lot of attention in the information field due to the data growth in the preceding ten years. A fundamental purpose of philosophical 'query optimisation' (QO) approaches in a BD environment is data retrieving. To offer beneficial and practical choices for BD query optimisation, numerous technologies that focus on the cloud have been developed. Existing significant data query optimisation approaches often struggle to efficiently process complex queries on massive datasets, leading to performance bottlenecks and resource wastage. Despite significant advancements in big data query optimisation, there remains a need for innovative techniques that can seamlessly handle diverse workloads and data distributions while optimising resource utilisation and query performance. To solve query optimisation issues, this paper suggests an altruistic whale optimisation algorithm. In the following stage, the AltWOA optimiser increases the total query processing effectiveness while ignoring the energy-efficient query techniques. The metrics classification and computation time are tested for various data sizes, instances, and dataset records. It is found that in terms of interpreting, execution, and retrieval durations, the suggested technique performs better than the alternatives. Inderscience Publishers - linking academia, business and industry through research

Title: AltWOA: enhancing query performance with clustering-based optimisation

Authors: Mursubai Sandhya Rani; N. Raghavendra Sai

Addresses: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India ' Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

Abstract: Big data (BD) is gaining a lot of attention in the information field due to the data growth in the preceding ten years. A fundamental purpose of philosophical 'query optimisation' (QO) approaches in a BD environment is data retrieving. To offer beneficial and practical choices for BD query optimisation, numerous technologies that focus on the cloud have been developed. Existing significant data query optimisation approaches often struggle to efficiently process complex queries on massive datasets, leading to performance bottlenecks and resource wastage. Despite significant advancements in big data query optimisation, there remains a need for innovative techniques that can seamlessly handle diverse workloads and data distributions while optimising resource utilisation and query performance. To solve query optimisation issues, this paper suggests an altruistic whale optimisation algorithm. In the following stage, the AltWOA optimiser increases the total query processing effectiveness while ignoring the energy-efficient query techniques. The metrics classification and computation time are tested for various data sizes, instances, and dataset records. It is found that in terms of interpreting, execution, and retrieval durations, the suggested technique performs better than the alternatives.

Keywords: big data; BD; altruistic whale optimisation algorithm; AltWOA; query optimisation; QO; fast Markov clustering algorithm.

DOI: 10.1504/IJCC.2024.143529

International Journal of Cloud Computing, 2024 Vol.13 No.6, pp.581 - 604

Received: 13 Jun 2023
Accepted: 25 Oct 2023
Published online: 30 Dec 2024 *

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Title: AltWOA: enhancing query performance with clustering-based optimisation

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