Applications of queueing theory in stream processing

Project level: Honours, PhD

Real-time stream processing is critical in a wide array of applications, such as fraud detection, Internet of Things, marketing and advertising. Stream processing has become essential in modern applications as an alternative to classical batch processing. This project aims to develop a stochastic process model using queueing analysis methods in applied probability. We are interested in developing an efficient stream processing algorithm by choosing the optimal watermarks and batch and trigger intervals.