The PhD thesis titled "Association Rule Mining in High-Performance Computing: A Comparative Study of Apollo-ARM and Distributed Techniques" by Mahtab Shahin investigates the application of association rule mining algorithms across domains like healthcare, meteorology, and transportation. It evaluates the performance, efficiency, and scalability of the Apollo-ARM algorithm compared to distributed association rule mining techniques in high-performance computing (HPC) environments.

The research focuses on the efficiency of Apollo-ARM in uncovering meaningful associations that drive decision-making in various fields. In healthcare, the algorithm identifies significant associations between patient attributes and disease outcomes, improving clinical decision-making and personalized treatment.

In meteorology, the study explores correlations between climate variables, uncovering hidden patterns for improved weather prediction and climate change studies. The transportation domain analysis reveals associations influencing traffic accidents, informing data-driven strategies for road safety.

Comparing Apollo-ARM with distributed techniques, Apollo-ARM outperformed in speed, scalability, and resource utilization, particularly in environments with limited computational resources. While distributed methods improve with more nodes, Apollo-ARM proves more efficient overall, especially where hardware scaling isn't feasible.

Mahtab Shahin’s research offers valuable insights into association rule mining applications across multiple sectors, demonstrating Apollo-ARM’s robust and scalable approach for extracting patterns from large datasets.

The thesis "Efficient and Effective Association Rule Mining on Big Data and Cloud Technology: A Multifaceted Analysis" is published in the Digital Collection of TalTech Library.

Supervisors:

• Prof. Dirk Draheim, Tallinn University of Technology, Estonia;
• Dr. Tara Ghasempouri, Tallinn University of Technology, Estonia;
• Dr. Syed Attique Shah, Birmingham City University, United Kingdom.

Oponents:

• Prof. Tania Cerquitelli, Politecnico di Torino, Italy;
• Prof. Arun Kumar Sangaiah, National Yunlin University of Science and Technology, Taiwan.

Follow public defence in Zoom

Meeting ID: 914 9209 4079
Passcode: 967603

After the defence, starting at 14:00 starting at 14:00 in room ICT-701, Prof. Tania Cerquitelli (Birmingham City University, UK) will give a presentation on "Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time" and starting at 15:00 in room ICT-701, Prof Arun Kumar Sangaiah (National Yunlin University of Science and Technology, Taiwan) will give a presentation on "AI-IoT-UAV on On-Board Intelligence with Navigation Exploration on Precision Agriculture".