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Big data: survey, technologies, opportunities, and challenges.

Khan N, Yaqoob I, Hashem IA, Inayat Z, Ali WK, Alam M, Shiraz M, Gani A - ScientificWorldJournal (2014)

Bottom Line: At this point, predicted data production will be 44 times greater than that in 2009.Future research directions in this field are determined based on opportunities and several open issues in Big Data domination.These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

View Article: PubMed Central - PubMed

Affiliation: Mobile Cloud Computing Research Lab, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia ; Department of Computer Science, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan.

ABSTRACT
Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

Show MeSH
System architectures of MapReduce and HDFS.
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Related In: Results  -  Collection


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fig4: System architectures of MapReduce and HDFS.

Mentions: To scale the processing of Big Data, map and reduce functions can be performed on small subsets of large datasets [56, 57]. In a Hadoop cluster, data are deconstructed into smaller blocks. These blocks are distributed throughout the cluster. HDFS enables this function, and its design is heavily inspired by the distributed file system Google File System (GFS). Figure 4 depicts the architectures of MapReduce and HDFS.


Big data: survey, technologies, opportunities, and challenges.

Khan N, Yaqoob I, Hashem IA, Inayat Z, Ali WK, Alam M, Shiraz M, Gani A - ScientificWorldJournal (2014)

System architectures of MapReduce and HDFS.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4127205&req=5

fig4: System architectures of MapReduce and HDFS.
Mentions: To scale the processing of Big Data, map and reduce functions can be performed on small subsets of large datasets [56, 57]. In a Hadoop cluster, data are deconstructed into smaller blocks. These blocks are distributed throughout the cluster. HDFS enables this function, and its design is heavily inspired by the distributed file system Google File System (GFS). Figure 4 depicts the architectures of MapReduce and HDFS.

Bottom Line: At this point, predicted data production will be 44 times greater than that in 2009.Future research directions in this field are determined based on opportunities and several open issues in Big Data domination.These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

View Article: PubMed Central - PubMed

Affiliation: Mobile Cloud Computing Research Lab, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia ; Department of Computer Science, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan.

ABSTRACT
Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

Show MeSH