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Orchestrating BigData Analysis Workflows

Lookup NU author(s): Professor Raj Ranjan, Dr Ellis SolaimanORCiD, Professor Philip James



This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2017.

For re-use rights please refer to the publisher's terms and conditions.


Data analytics has become not only an essential part of day-to-day decision making, but also reinforces long term strategic decisions. Whether it is real-time fraud detection, resource management, tracking and prevention of disease outbreak, natural disaster management or intelligent traffic management, the extraction and exploitation of insightful information from unparalleled quantities of data (BigData) is now a fundamental part of all decision making processes. Success in making smart decisions by analyzing BigData is possible due to the availability of improved analytical capabilities, increased access to different data sources, and cheaper and improved computing power in the form of cloud computing. However, BigData analysis is far more complicated than the perception created by the recent publicity. For example, one of the myths is that BigData analysis is driven purely by the innovation of new data mining and machine learning algorithms.

Publication metadata

Author(s): Ranjan R, Garg S, Khoskbar A, Solaiman E, Philip J, Georgakopoulos D

Publication type: Article

Publication status: Published

Journal: IEEE Cloud Computing

Year: 2017

Volume: 4

Issue: 3

Online publication date: 29/06/2017

Acceptance date: 31/05/2017

Date deposited: 22/11/2017

ISSN (electronic): 2325-6095

Publisher: IEEE


DOI: 10.1109/MCC.2017.55


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