FAIR: A Hadoop-based Hybrid Model for Faculty Information Retrieval System

TL;DR

This paper presents a Hadoop-based hybrid system leveraging Apache Spark, Hive, and Tableau to efficiently retrieve and visualize faculty research data across multiple colleges, addressing the challenge of big data in academic information management.

Contribution

It introduces a novel distributed model combining Spark, Hive, and Tableau for centralized faculty information retrieval and visualization in a big data environment.

Findings

Efficient retrieval of faculty research data across multiple colleges.

Effective visualization of large-scale academic information.

Demonstrated scalability in a distributed computing environment.

Abstract

In era of ever-expanding data and knowledge, we lack a centralized system that maps all the faculties to their research works. This problem has not been addressed in the past and it becomes challenging for students to connect with the right faculty of their domain. Since we have so many colleges and faculties this lies in the category of big data problem. In this paper, we present a model which works on the distributed computing environment to tackle big data. The proposed model uses apache spark as an execution engine and hive as database. The results are visualized with the help of Tableau that is connected to Apache Hive to achieve distributed computing.