Big Data Management: An Investigation into Wireless and Cloud Computing

Big Data Management: An Investigation into Wireless and Cloud Computing

Sunny Orike (Department of Electrical/Computer Engineering, Rivers State University of Science and Technology, Port Harcourt, Nigeria) and Daboso Brown (Department of Information Technology, National Open University of Nigeria, Port Harcourt Center, Port Harcourt, Nigeria)
DOI: 10.4018/IJITN.2016100104
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Organizations and governments leverage on the potentials in data to plan and compete globally. Data from various sources are continually mined, stored in databases and utilized in a manner that improves processes, products and ensure steady profitability. The traditional relational database management systems are unable to cope with these new forms of data. The velocity, volume and variety in which data are generated qualify them as “Big Data”. This scales up the storage and processing needs of client organizations, allowing them to focus on their core areas of expertise. This paper investigates the relevance of aggregating big data and its efficient management in a cloud computing environment. It adopts the prototyping design and a case study approach, based on Couchbase Server, a non-structured query language document database and cloud computing platform. The accruable benefit of this work is organizational cost-effectiveness, timeliness in decision making and higher profitability.
Article Preview

Introduction

The amount of data deployed for business solutions in the recent times has been on an exponential increase (Hurwitz et al., 2013). This has attracted several interests in the field of big data analytics. Its relevance cut across several areas of human endeavor (Schwartz, Joshua & Poore, 2014; Wang & Alexander, 2015). Organizations capture trillions of bytes of data from different sources and in various formats, including: social media, networked sensors, machine-generated data (x-ray, scanning devices, airplane engines, etc), spatial-coordinates, mobile phones, the cloud etc. The traditional relational databases are unable to handle these varied data sources due to their semi-structured/unstructured nature. Big data consists of large pools of data that are captured, communicated, aggregated, stored, and analyzed in diverse ways (Assuncao et al., 2015; Chen, Chiang & Storey, 2012; Ji et al., 2012; Russom, 2013). It is increasingly becoming an integral part of modern economic activities, and a mastermind of the wireless telecommunications sector. Big data involves high-volume, high-velocity, and high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization. It is about how data from various heterogeneous sources are captured, organized, integrated, and analyzed in a timely manner in order to make actionable organizational decisions. Big data requires clusters of servers to support the tools that process varied formats of data. This informs the necessity for big data to be deployed in cloud computing environments.

In cloud computing, huge modern day scalable computing resources, applications, platforms and storage are made available to organizations on a ‘pay-per-use’ basis. It provides economies of scale in an on-demand basis over the internet, and eliminates the need for a separate full-fledged computing department, thereby freeing up resources for organizations to focus on their core area of business endeavor. Although cloud computing concepts such as distributed systems, grid computing and concurrent programming have been around for a while, current global business solutions, coupled with virtualization facilitated the business model of clouds to evolve and enabled its widespread roll out technology (Assuncao et al., 2015; Chen & Zhao, 2012; Hofer and Karagiannis, 2011; Ji et al., 2012; Nazir, 2012). Virtualization technology is a technique whereby a single physical machine can host multiple virtual machines, ensuring a maximum utilization of cloud hardware and scalability, which leads to maximization of capital investment in the cloud. Modern big data technologies with cloud technologies have devised solutions to the limitations in processing and analyzing massively parallel data sets using cost effective techniques.

Businesses, organizations and governmental agencies (including the wireless telecommunications sector), are faced with problems of making timely and meaningful decisions that ought to impact on their overall goal on a continual basis (Chen, Chiang & Storey, 2012). Mere intuition and experience can no longer be relied upon in this fast-changing and fiercely competitive modern world for such critical decisions, hence the need for methodologies to incorporate big data from the various data channels and its subsequent processing by the utilization of the cost-effective and scalable power of cloud computing technology.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing