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GraphJ: A Tool for Big Data Complexity Reduction

Authors:
Hani Bani-Salameh
Abdullah Al-Shishani

Keywords: Big data; Reduction; Complexity; Graph; Relational database; Neo4J; GraphJ.

Abstract:
Software developers, researchers, and industrial companies from all sectors such health, transportation, water treatment, etc., use and deal with big data in order to conduct their research and find better solutions that improve our way of life. Data scientists and software engineers are using generated big data to get accurate information and to extract the maximum value from the data available to them. Big data is applicable in many domains and can help solve many problems. However, analyzing such data is not easy due to its complexity that is resembled by the 6Vs of big data: volume, velocity, value, variety, variability, and veracity. Thus, big data reduction methods and tools are used in order to enhance the data and make it easier to analyze. This paper presents a big data complexity reduction tool called GraphJ. The proposed tool converts a rational database into a graph database, which makes unlocking knowledge patterns much easier than dealing with ordinary rational databases. A case study has been conducted to assess the usefulness and effectiveness of the proposed tool.

Pages: 20 to 24

Copyright: Copyright (c) IARIA, 2019

Publication date: March 24, 2019

Published in: conference

ISSN: 2519-8386

ISBN: 978-1-61208-700-9

Location: Valencia, Spain

Dates: from March 24, 2019 to March 28, 2019