Leveraging Cloud Technology for Enhanced Decision-Making Using the Weighted Product Model

Big data security is an essential aspect of modern information systems, ensuring the protection of sensitive data while maintaining system functionality and user trust. This study leverages the Weighted Product Model (WPM) to evaluate various approaches to big data security, drawing parallels to the performance evaluation of alternative solutions in critical systems like helicopters. By analyzing key parameters such as autonomy, versatility, availability, and price, the research highlights the challenges and opportunities in designing robust security frameworks that balance performance and cost-effectiveness. Research Significance: Big data security is pivotal for safeguarding sensitive information in industries such as healthcare, finance, and defense. The growing reliance on cloud computing and IoT has amplified vulnerabilities, making it critical to develop frameworks that address data privacy, integrity, and secure access. This study underscores the significance of evaluating and comparing alternative approaches to improve the dependability and scalability of large-scale data systems while guaranteeing compliance with security standards. Methodology: WPM: The Weighted Product Model is employed to assess and order options according to pre-established criteria. This multi-criterion decision-making methodology allows for a comprehensive assessment of each alternative’s performance across autonomy, versatility, availability, and price. Each criterion is weighted equally to ensure an unbiased comparison, and the alternatives are scored based on their relative performance to determine the most suitable option. Alternative: Airbus EC 135: A compact, twin-engine solution with excellent maneuverability and moderate cost-effectiveness. Bell 206 (Long Ranger): A highly versatile and reliable single-engine model, widely used for various operations. Leonardo AW 139: A medium-sized, twin-engine option known for its range and advanced features, suitable for specialized missions. Robinson 44: A cost-effective, light helicopter popular for training and private use, offering high availability. Sikorsky UH-60 (Black Hawk): A durable, multi-role military helicopter with superior autonomy and protection but higher costs. Evaluation Parameter: Autonomy (minutes): The duration the system or solution can operate without interruption. Versatility: The ability to adapt to various use cases and operational environments. Availability: Readiness and ease of deployment in critical scenarios. Price (US$): The cost-effectiveness, including acquisition and maintenance expenses. Result: The Bell 206 Long Ranger emerged as the top performer, excelling in versatility, availability, and overall cost-effectiveness, making it the preferred option for diverse applications. The Robinson 44 ranked second, favored for its affordability and high availability. The Airbus EC 135 secured the third position, striking a balance between cost and performance for specific roles. The Sikorsky UH-60 Black Hawk, with its superior autonomy, ranked fourth but was limited by high costs and specialization. The Leonardo AW 139, while advanced, ranked fifth due to its high price and limited flexibility. This analysis showcases the importance of balancing performance and cost in selecting security solutions for big data systems.