Issues and Challenges in Convergence of Big Data, Cloud and Data Science

Mathur, Neha; Purohit, Rajesh

doi:10.5120/ijca2017913082

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…Despite several businesses having adopted cloud computing, organizational applications continue to generate new problems. Some important security challenges are data storage, management, transmission, analysis, processing, integration, visualization, privacy, security, quality, scalability, availability and heterogeneity (Majhi and Shial, 2015;Mathur and Purohit, 2017;Yang et al, 2017b). This section examines diverse problems and difficulties of cloud computing with big data.…”

Section: Overviewmentioning

confidence: 99%

How does cloud computing help businesses to manage big data issues

Latifian

2022

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PurposeBig data has posed problems for businesses, the Information Technology (IT) sector and the science community. The problems posed by big data can be effectively addressed using cloud computing and associated distributed computing technology. Cloud computing and big data are two significant past-year problems that allow high-efficiency and competitive computing tools to be delivered as IT services. The paper aims to examine the role of the cloud as a tool for managing big data in various aspects to help businesses.Design/methodology/approachThis paper delivers solutions in the cloud for storing, compressing, analyzing and processing big data. Hence, articles were divided into four categories: articles on big data storage, articles on big data processing, articles on analyzing and finally, articles on data compression in cloud computing. This article is based on a systematic literature review. Also, it is based on a review of 19 published papers on big data.FindingsFrom the results, it can be inferred that cloud computing technology has features that can be useful for big data management. Challenging issues are raised in each section. For example, in storing big data, privacy and security issues are challenging.Research limitations/implicationsThere were limitations to this systematic review. The first limitation is that only English articles were reviewed. Also, articles that matched the keywords were used. Finally, in this review, authoritative articles were reviewed, and slides and tutorials were avoided.Practical implicationsThe research presents new insight into the business value of cloud computing in interfirm collaborations.Originality/valuePrevious research has often examined other aspects of big data in the cloud. This article takes a new approach to the subject. It allows big data researchers to comprehend the various aspects of big data management in the cloud. In addition, setting an agenda for future research saves time and effort for readers searching for topics within big data.

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Section: Overviewmentioning

confidence: 99%

How does cloud computing help businesses to manage big data issues

Latifian

2022

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“…Data governance provides the aims, policies, regulations, guidelines, tools, and solutions for ensuring successful data management activities. Mathur and Purohit ( 2017) argued that it is necessary to deal with the main problems of access, metadata, utilization, update, governance, and reference. Ranjan et al ( 2018) designed data management components, including data governance, data analysis, and data warehousing from the perspective of the Internet of Things ( IoT).…”

Section: Data Management and Data Governancementioning

confidence: 99%

Data science: Trends, perspectives, and prospects

2022

DSI

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Data science is gaining momentum across a range of disciplines. The term "data science" can be traced back to 1974 when the computer scientist Peter Naur coined and defined it as the science of dealing with data ( Naur, 1974) , and then data science first occurred as a scientific idea in computer science. In 2001, William S. Cleveland, a statistician, proposed an action plan for expanding the technical areas of the field of statistics ( Cleveland, 2001) , and statistics was the second discipline that delineated data science. Hence, computer science and statistics are the two main theoretical foundations of data science. In 2010, Drew Conway, the founder of Alluvium, published a data science Venn diagram and first discussed the interdisciplinary of data science. He argued that data science is located at the intersection of hacking skills, math and statistics knowledge, and substantive expertise. Further, this Venn

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“…It takes longer to transmit big data over current networks from a collection or storage point to a processing point, than it takes to process it. For example, transferring an exabyte of data can take a few thousand hours over a fast network with a sustained data transfer rate [21]. Several authors support, "bringing the code to the data", unlike the traditional method of "bringing the data to the code" [22].…”

Section: Data Storage and Transport (Dst 27mentioning

confidence: 99%

Big Data Redux: New Issues and Challenges Moving Forward

Espinosa

Kaisler

Armour

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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As of the time of this writing, our HICSS-46 proceedings article has enjoyed over 520 Google Scholar citations. We have published several HICSS proceedings, articles and a book on this subject, but none of them have generated this level of interest. In an effort to update our findings six years later, and to understand what is driving this interest, we have downloaded the first 500 citations to our article and the corresponding citing article, when available. We conducted an in-depth literature review of the articles published in top journals and leading conference proceedings, along with articles with a high volume of citations. This paper provides a brief summary of the key concepts in our original paper and reports on the key aspects of interest we found in our review, and also updates our original paper with new directions for future practice and research in big data and analytics.

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Issues and Challenges in Convergence of Big Data, Cloud and Data Science

Cited by 8 publications

References 2 publications

How does cloud computing help businesses to manage big data issues

How does cloud computing help businesses to manage big data issues

Data science: Trends, perspectives, and prospects

Big Data Redux: New Issues and Challenges Moving Forward

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