This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems.

Using advanced data mining techniques, specifically association rule mining (ARM) and clustering, this study presents a novel approach to maritime CO2 emissions analysis, revealing hidden patterns and relationships that traditional statistical models cannot capture. Various ship types can be analyzed for operational and technical efficiency to provide actionable insights into reducing emissions. In addition, robust cybersecurity measures are integrated to ensure the integrity and reliability of the data, allowing compliant and secure decision-making. The findings indicate that oil tankers and LNG carriers, which emit significant amounts of pollution, are prime candidates for retrofitting and implementing cleaner technologies in the near future.

Accurate weather and climate prediction is essential for early warning systems that improve response strategies to climate-related events. This study explores the use of association rule mining (ARM) techniques to analyze large-scale meteorological datasets. We focus on the weather patterns of Tallinn and Tartu, investigating variables such as wind speed, temperature, precipitation, and humidity, and their influence on weather intensity. A distributed ARM approach is employed using the Apollo framework, which utilizes serverless functions to enhance scalability and performance. Results show Apollo outperforms traditional systems like Apache Spark by approximately 15% in terms of processing speed, while extracting a greater number of meaningful rules. Time-series analysis was also applied to investigate temporal weather trends. Our findings highlight the potential of this approach for enhancing weather prediction systems and offer a foundation for future research in this area.

Lung cancer, a critical global health issue, necessitates advanced data analysis for better detection, diagnosis, and treatment personalization. The purpose of this study is to enhance Apriori's algorithm for distributed computing using Apollo's multi-cloud orchestration framework to address challenges associated with large-scale healthcare datasets. We have demonstrated significant improvements in performance and scalability compared to traditional approaches. Based on our evaluation of real-world lung cancer data, we demonstrated substantial improvements in computational efficiency and data mining capabilities, paving the way for improved insights and customized treatments and contributing to oncology research through efficient large-scale data analysis.

Early diagnosis and accurate judgment are paramount in cancer diagnosis and treatment. Establishing an effective cancer early warning system is crucial to improve patient outcomes. Data mining technology, particularly association rule mining, plays a vital role in cancer surveillance and early warning by processing large datasets. Our study focuses on lung cancer, one of the leading causes of death worldwide. Despite numerous approaches, challenges such as high computational costs and memory limitations persist when attempting to extract meaningful rules from databases. In this paper, we propose leveraging the Apriori algorithm within the Apollo framework, based on the Apollo multicloud orchestration framework developed by the University of Innsbruck, for distributed association rule mining. By harnessing serverless functions, we achieve distributed processing, enhancing scalability and performance. Our experiments demonstrate that Apollo outperforms Apache Spark in terms of speed (about 15 percent), and extracts more rules. The results highlight the efficacy of distributed association rule mining using serverless functions for cancer early warning systems. We conclude that this approach shows promise and warrants further exploration and extension in future research endeavors.