Date Approved

12-12-2024

Graduate Degree Type

Project

Degree Name

Computer Information Systems (M.S.)

Degree Program

School of Computing and Information Systems

First Advisor

Christian Trefftz

Academic Year

2024/2025

Abstract

The PageRank algorithm is introduced by Google, It is used in search engine optimization, and ranking web pages based on their significance and importance based on a graph structure. This project focuses on implementing the PageRank algorithm using Java and Spring Boot, with Apache Spark as the core technologies for graph data processing. The project aims to compute PageRank scores for nodes in a directed graph, simulating real-world scenarios such as web page ranking and link analysis. The implementation is designed to handle large datasets efficiently by leveraging Spark's distributed computing capabilities and GraphFrames' robust graph processing framework. The workflow includes data ingestion, graph construction, iterative PageRank computation, and storage of results in a relational database for further analysis. To ensure accessibility, the computed PageRank scores are exposed via RESTful APIs developed using Spring Boot, enabling seamless integration with other applications or visualization tools. This project demonstrates the integration of advanced graph processing algorithms within a modular and scalable architecture. Key contributions include the application of distributed computing to enhance the performance of graph-based algorithms, the development of a flexible backend using Spring Boot, and the provision of user-friendly interfaces for querying and analyzing PageRank scores. The system's extensibility allows it to be adapted for various use cases, such as social network analysis, recommendation systems, and influence modeling. By combining the computational efficiency of Apache Spark, the structural advantages of GraphFrames, and the robust framework of Spring Boot, this project illustrates a practical approach to solving graph-based problems in modern software systems.

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