Key research themes
1. How can stream processing systems be designed and optimized for low-latency, scalable, and reliable real-time data analytics?
This theme investigates architectural principles, control mechanisms, and processing models for building efficient distributed stream processing systems that operate under the constraints of dynamic workloads, high throughput, and low latency, enabling real-time analytics over continuous data streams.
2. What are the mathematical and execution semantics models that can unify and explain heterogeneous behaviors of stream processing engines?
This research area investigates formal descriptive models to characterize and compare the diverse execution semantics of various academic and commercial stream processing systems (SPEs), particularly for windowing and query evaluation, thereby addressing heterogeneity in syntax, capabilities, and execution dynamics to improve portability and predictability of streaming applications.
3. How can hydrological streamflow be efficiently and accurately measured using modern non-contact sensing and computational techniques?
This theme focuses on developing and validating affordable, non-invasive measurement methods for streamflow monitoring employing techniques such as beamforming on video sensing, surface velocity radar, telemetry networks, and applying statistical models to estimate flow attributes, crucial for hydrological studies, flood prevention, and water resources management especially in regions with observational scarcity.