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The rise of “big data” on cloud computing

The continuous increase in the volume and detail of data captured by organizations, such as the rise of social media, Internet of Things (IoT), and multimedia, has produced an overwhelming flow of data in either structured or unstructured format. 

1. Introduction

The continuous increase in the volume and detail of data captured by organizations, such as the rise of social media, Internet of Things (IoT), and multimedia, has produced an overwhelming flow of data in either structured or unstructured format. Data creation is occurring at a record rate [1], referred to herein as big data, and has emerged as a widely recognized trend. Big data is eliciting attention from the academia, government, and industry. Big data are characterized by three aspects: (a) data are numerous, (b) data cannot be categorized into regular relational databases, and (c) data are generated, captured, and processed rapidly. Moreover, big data is transforming healthcare, science, engineering, finance, business, and eventually, the society. The advancements in data storage and mining technologies allow for the preservation of increasing amounts of data described by a change in the nature of data held by organizations [2]. The rate at which new data are being generated is staggering [3]. A major challenge for researchers and practitioners is that this growth rate exceeds their ability to design appropriate cloud computing platforms for data analysis and update intensive workloads. Cloud computing is one of the most significant shifts in modern ICT and service for enterprise applications and has become a powerful architecture to perform large-scale and complex computing. The advantages of cloud computing include virtualized resources, parallel processing, security, and data service integration with scalable data storage. Cloud computing can not only minimize the cost and restriction for automation and computerization by individuals and enterprises but can also provide reduced infrastructure maintenance cost, efficient management, and user access [4]. As a result of the said advantages, a number of applications that leverage various cloud platforms have been developed and resulted in a tremendous increase in the scale of data generated and consumed by such applications. Some of the first adopters of big data in cloud computing are users that deployed Hadoop clusters in highly scalable and elastic computing environments provided by vendors, such as IBM, Microsoft Azure, and Amazon AWS [5]. Virtualization is one of the base technologies applicable to the implementation of cloud computing. The basis for many platform attributes required to access, store, analyze, and manage distributed computing components in a big data environment is achieved through virtualization. Virtualization is a process of resource sharing and isolation of underlying hardware to increase computer resource utilization, efficiency, and scalability. The goal of this study is to implement a comprehensive investigation of the status of big data in cloud computing environments and provide the definition, characteristics, and classification of big data along with some discussions on cloud computing. The relationship between big data and cloud computing, big data storage systems, and Hadoop technology are discussed. Furthermore, research challenges are discussed, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance. Several open research issues that require substantial research efforts are likewise summarized. The rest of this paper is organized as follows. Section 2 presents the definition, characteristics, and classification of big data. Section 3 provides an overview of cloud computing. The relationship between cloud computing and big data is presented in Section 4. Section 5 presents the storage systems of big data. Section 6 presents the Hadoop background and MapReduce. Several issues, research challenges, and studies that have been conducted in the domain of big data are reviewed in Section 7. Section 8 provides a summary of current open research issues and presents the conclusions. Table 1 shows the list of abbreviations used in the paper.

2. Definition and characteristics of big data

Big data is a term utilized to refer to the increase in the volume of data that are difficult to store, process, and analyze through traditional database technologies. The nature of big data is indistinct and involves considerable processes to identify and translate the data into new insights. The term “big data” is relatively new in IT and business. However, several researchers and practitioners have utilized the term in previous literature. For instance, [6] referred to big data as a large volume of scientific data for visualization. Several definitions of big data currently exist. For instance,defined big data as “the amount of data just beyond technology's capability to store, manage, and process efficiently.” Meanwhile, and  defined big data as characterized by three Vs: volume, variety, and velocity. The terms volume, variety, and velocity were originally introduced by Gartner to describe the elements of big data challenges. IDC also defined big data technologies as “a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling the high velocity capture, discovery, and/or analysis.” specified that big data is not only characterized by the three Vs mentioned above but may also extend to four Vs, namely, volume, variety, velocity, and value (Fig. 1, Fig. 2). This 4V definition is widely recognized because it highlights the meaning and necessity of big data. The following definition is proposed based on the abovementioned definitions and our observation and analysis of the essence of big data. Big data is a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex, and of a massive scale. Volume refers to the amount of all types of data generated from different sources and continue to expand. The benefit of gathering large amounts of data includes the creation of hidden information and patterns through data analysis. Laurila et al. provided a unique collection of longitudinal data from smart mobile devices and made this collection available to the research community. The aforesaid initiative is called mobile data challenge motivated by Nokia . Collecting longitudinal data requires considerable effort and underlying investments. Nevertheless, such mobile data challenge produced an interesting result similar to that in the examination of the predictability of human behavior patterns or means to share data based on human mobility and visualization techniques for complex data.  Variety refers to the different types of data collected via sensors, smartphones, or social networks. Such data types include video, image, text, audio, and data logs, in either structured or unstructured format. Most of the data generated from mobile applications are in unstructured format. For example, text messages, online games, blogs, and social media generate different types of unstructured data through mobile devices and sensors. Internet users also generate an extremely diverse set of structured and unstructured data . Velocity refers to the speed of data transfer. The contents of data constantly change because of the absorption of complementary data collections, introduction of previously archived data or legacy collections, and streamed data arriving from multiple sources . Value is the most important aspect of big data; it refers to the process of discovering huge hidden values from large datasets with various types and rapid generation.

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Last modified on Sunday, 08 April 2018 19:21
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