PageRank for billion-scale networks in RDBMS

Abstract

Data processing for Big Data plays a vital role for decision-makers in organizations and government, enhances the user experience, and provides quality results in prediction analysis. However, many modern data processing solutions make a significant investment in hardware and maintenance costs, such as Hadoop and Spark, often neglecting the well established and widely used relational database management systems (RDBMS’s). PageRank is vital in Google Search and social networks to determine how to sort search results and how influential a person is in a social group. PageRank is an iterative algorithm which imposes challenges when implementing it over large graphs which are becoming the norm with the current volume of data processed everyday from social networks, IOT, and web content. In this paper we study computing PageRank using RDBMS for very large graphs using a consumer-grade server and compare the results to a dedicated graph database.