Published Paper Details:

Cloud computing has become an essential paradigm in modern IT infrastructure, providing flexible and scalable services to organizations and individuals. With the rapid growth of cloud service offerings, it has become increasingly challenging for users to identify the most suitable services and understand their relationships with other services. Association rule mining techniques have been widely used to discover meaningful patterns and relationships in various domains. In this study, we propose a novel approach called Priority Based Association Rule Mining (PBARM) to explore cloud services and their associations based on user-defined priorities. PBARM leverages user-defined priorities to guide the association rule mining process and extract relevant patterns and relationships. Users can specify their preferences and requirements regarding service characteristics, such as cost, performance, security, and availability. These priorities are then used to assign weights to different attributes and determine the importance of each attribute in the association rule generation process. By considering user-defined priorities, PBARM enables users to focus on the most relevant associations based on their specific needs. To evaluate the effectiveness of PBARM, we conducted experiments using a real-world dataset of cloud services. The results demonstrate that PBARM outperforms traditional association rule mining approaches in terms of relevance and accuracy of the discovered associations. By incorporating user-defined priorities, PBARM provides a more personalized and targeted exploration of cloud services, assisting users in making informed decisions and optimizing their cloud service selections. Furthermore, we developed a user-friendly visualization tool that presents the discovered associations in an intuitive manner, allowing users to interactively explore the relationships between different cloud services. The tool enables users to adjust their priorities dynamically and observe the impact on the generated associations, enhancing the flexibility and usability of the exploration process. In conclusion, PBARM offers a novel approach to explore cloud services and their associations by incorporating user-defined priorities. By considering user preferences and requirements, PBARM provides personalized and relevant recommendations, facilitating decision-making in cloud service selection. The proposed approach and visualization tool contribute to enhancing the usability and efficiency of cloud service exploration, ultimately improving the overall cloud computing experience for users