This paper introduces a novel framework that leverages ontology engineering to enhance big data science, addressing challenges such as data heterogeneity, integration, and scalability. The framework employs semantic technologies to unify diverse datasets, enabling advanced knowledge representation, reasoning, and predictive modelling. By integrating ontological methods with big data analytics, the framework demonstrates improved data integration and analysis across multiple domains. Case studies in big data management and healthcare systems illustrate the practical applications and benefits of the framework, including enhanced data quality, semantic alignment, and process efficiency. This work advances the field of big data science by providing a scalable and interoperable solution for managing complex datasets, paving the way for future research and applications in data-driven domains.

In this paper, we introduce the Chaotic Dynamic Cat Swarm Optimization (CHDCSO) algorithm, an enhanced variant of the traditional Cat Swarm Optimization (CSO), to address the Traveling Salesman Problem (TSP). By integrating chaotic dynamics using logistic chaotic maps, the proposed algorithm aims to improve the balance between exploration and exploitation phases, thereby enhancing the global search capability and robustness of the optimization process. Detailed experimental analysis is conducted on various TSP datasets, comparing the performance of CHDCSO against established optimization techniques, including Particle Swarm Optimization (PSO), Differential Evolution (DDE), Transit Search, Random Optimization Algorithm (ROA), and Random Search Algorithm (RSA). The results demonstrate that the CHDCSO algorithm consistently outperforms these traditional methods in terms of convergence speed and solution quality. This study underscores the potential of chaotic dynamics in optimizing complex problems and highlights the CHDCSO algorithm’s efficacy in achieving superior optimization outcomes.

This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.