Temporal Analysis of Egocentric Email Network by using Graph Database

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K . NUSRATULLAH
A. SHAH
N. KHAN

Abstract

Necessity is the mother of invention. It is a very famous proverb and hundred percent true in the case of computer technology. Computer scientists are extensively involve to invent technology that fulfills the requirement of exploring today’s big data. Today technology is not only needed to store and manage data, it’s more needed to analyze and discover knowledge from data. Data analysis is being considered as a major evaluation criteria to dig useful information. It supports organizations to lead smarter moves and more efficient operations on the basis of knowledge evaluation. Egocentric analysis of email networks on temporal basis is the striking field that results thought-provoking analysis for a personal network. Such networks revolve around the single individual and its relationship and depict interesting changes in an individual’s network as the time passed by. Technologies like relational databases, NOSQL, Hadoop, EgoLines are working as the analytical tools to capture the provocative timely changes of egocentric networks. Among them an emerging technology called Graph Databases is getting popular as an efficient tool to depict better analysis in the said fields. It is being observed that graph databases are like the next generation of relational databases with proficient support for “relationships, flexible and fine-grained data model that allows modeling and managing rich domains in an easy and intuitive way. The intension of this work is to present a review study of GDBs in order to identify the success graph of this emerging technology in the field of data analysis. Also, it is an effort to investigate the uttermost functionality of the GDBS in order to analyze the egocentric email networks on temporal basis.

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How to Cite
K . NUSRATULLAH, A. SHAH, & N. KHAN. (2018). Temporal Analysis of Egocentric Email Network by using Graph Database . Sindh University Research Journal - SURJ (Science Series), 50(3D). https://doi.org/10.26692/surj.v50i3D.1166
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