Big Data is ubiquitous. Useful information that can empower the end-users to gain a competitive advantage and achieve socio-economic development lies hidden in this data. The Sustainable Ambient Computing Lab (SAC-Lab) aims to explore sustainable and practicable solutions (through novel mathematical models and fast algorithms) to discover useful information in big data.
With a focus on sustainability, our lab recognizes the importance of green computing and strives to develop energy-efficient computing systems and practices that minimize environmental impact. We aim to contribute to a greener, more sustainable digital landscape by leveraging advanced algorithms and optimizing hardware utilization. Our works align with the principles of Society 5.0, which seeks to harmonize economic advancement with social challenges and environmental sustainability.
At the core, our research lies in high-performance computing, big data analytics, and pattern mining. Our work is of two folds:
- Develop state-of-the-art energy-efficient and fast algorithms to discover interesting patterns in big data. We do this by leveraging technologies like CUDA, PySpark, and Open MPI.
- Investigate novel theoretical models to discover useful information in (certain, uncertain) x (static, stream, graph) data.
Our publications and software development efforts have yielded valuable insights into scalable and efficient data mining techniques. We actively advance knowledge discovery and foster innovation in the field through collaborations with esteemed domestic and international institutions.