Thesis Abstract

This project focuses on optimizing distributed computing algorithms for real-time analytics in large-scale data processing systems. The project aims to improve the efficiency and performance of distributed computing frameworks by leveraging parallel processing, data partitioning, and load balancing techniques. The optimized algorithms will enable real-time processing of streaming data and complex analytics tasks, making them suitable for applications such as financial trading, sensor data processing, and online advertising. The project will contribute to the advancement of real-time analytics in distributed computing environments, addressing the increasing demand for timely insights from large volumes of data.

Thesis Overview