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Title: Big Data : coping with data obesity in cloud environments
Author: McCaul, Christopher Francis
ISNI:       0000 0004 6425 7091
Awarding Body: Ulster University
Current Institution: Ulster University
Date of Award: 2017
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The evolution of new technologies to process data is ever growing. This can be attributed to a number of factors such as the volume of existing data, the frequency with which new data is being generated or the understanding that processing data faster increases its worth. Regardless, research indicates that the volume of global data will continue to grow exponentially, much faster than the implementation of technologies to transport it. In parallel to this data proliferation, the Cloud Computing model has emerged, providing users with access to much greater processing power on a pay-per-use basis. As such, this has resulted in a convergence of the two technologies, dubbed Big Data as a service, facilitating the processing of ever-growing volumes of data at a fraction of the previous cost. It is thus evident that Big Data will continue to grow and be used in conjunction with Cloud Computing. Current research on Cloud-hosted Big Data processing typically focuses on specific scenarios, relying heavily on Cloud environments to process all of the available data and failing to consider the effect the volume and frequency of this data will have on Cloud systems. This thesis investigates the impact Big Data will have on the public network infrastructure, on which the Cloud computing model is critically reliant, with a particular focus on the most likely sources of this data i.e. the Internet of Things. As a result, the available bandwidth is identified to be a critical resource in such systems. This thesis continues on through experimentation to quantify the potential volume and impact of data generated for a prospective application, Smart Health, the results of which were used to develop a generic system architecture. The key outcome of this body of work is the presented system architecture which has the potential to significantly reduce the overall volume and frequency of data being transmitted to Cloud Big Data environments. It does so through a combination of data classification, offline storage of non-critical data and data summarisation prior to transmission, whilst maintaining system purpose and quality of service, and could potentially provide researchers with access to an expansive anonymised data source, accelerating research, development and deployment.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available