The 51 V's Of Big Data

Khan, Nawsher; Naim, Arshi; Hussain, Mohammad Rashid; Naveed, Quadri Noorulhasan; Ahmad, Naim; Qamar, Shamimul

doi:10.1145/3312614.3312623

Cited by 41 publications

(16 citation statements)

References 14 publications

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“…Wireless Communications and Mobile Computing extended as 4 V's, 5 V's, 7 V'S, 10 V's, 13 V's, 42 V's, 44 V's, and 51 V's [77,78], as shown in Table 2. 6.2. Process Challenges.…”

Section: Big Data Challengesmentioning

confidence: 99%

“…Data visualization is one of the essential instruments for determining a qualitative understanding. It can be used to convey and highlight the key relationships in layouts and charts that are more useful to stakeholders to measure the correlation or significance [46,54,78,90]. This might be useful when examining a dataset and retrieving features about it, and spotting patterns, corrupt data, outliers, and other things [38].…”

Section: Tools For Big Data Analysismentioning

confidence: 99%

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Big Data Analytics, Processing Models, Taxonomy of Tools, V’s, and Challenges: State-of-Art Review and Future Implications

Dasari,

Kaluri

2023

Wireless Communications and Mobile Computing

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In the current digital era, data is budding tremendously from various sources like banks, businesses, education, entertainment, etc. Due to its significant consequence, it became a prominent proceeding for numerous research areas like the semantic web, machine learning, computational intelligence, and data mining. For knowledge extraction, several corporate sectors depend on tweets, blogs, and social data to get adequate analysis. It helps them predict the customer’s tastes and preferences, optimize the usage of resources. In some cases, the same data creates complications that lead to a problem named as big data. To solve this, so many researchers have given various solutions. Based on literature analysis formulated 6-s simulation towards big data, detailed information about characteristics, a taxonomy of tools, and discussed various processing paradigms. No one tool can truly fit for all solutions, so this paper helps to make decisions smoothly by providing enough information and discussing major privacy issues and future directions.

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“…Wireless Communications and Mobile Computing extended as 4 V's, 5 V's, 7 V'S, 10 V's, 13 V's, 42 V's, 44 V's, and 51 V's [77,78], as shown in Table 2. 6.2. Process Challenges.…”

Section: Big Data Challengesmentioning

confidence: 99%

Section: Tools For Big Data Analysismentioning

confidence: 99%

Big Data Analytics, Processing Models, Taxonomy of Tools, V’s, and Challenges: State-of-Art Review and Future Implications

Dasari,

Kaluri

2023

Wireless Communications and Mobile Computing

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“…Although the 3Vs have been well-accepted for several years, other authors have added the features of veracity, value and variability ( Andreu Perez et al., 2015 ; De Mauro et al., 2016 ). Over the last few years, more dimensions have been added to the definition of big data as unique ( Khan et al., 2019b ) ( Farooqi et al., 2019 ) ( Shafer, 2017 ). It is important to note that conceiving each dimension of big data is entirely domain and application dependant.…”

Section: Related Workmentioning

confidence: 99%

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Hassan

Dhali

Zaman

et al. 2021

Heliyon

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Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology.

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“…In NIST it is stated that big data "consists of extensive datasets primarily in the characteristics of volume, velocity, variety, and/or variability that require a scalable architecture for efficient storage, manipulation, and analysis" [3]. Besides those four characteristics, which are also often referred to as the Vs of big data, due to their common initial letter, there are numerous more being mentioned in the literature, as, for instance, in [26], where 51 of those Vs are proposed.…”

Section: Characteristicsmentioning

confidence: 99%

Quality Assurance in Big Data Engineering - A Metareview

Staegemann

Volk

Turowski

2021

J. Complex Syst. Inform. Model. Q.

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With a continuously increasing amount and complexity of data being produced and captured, traditional ways of dealing with their storing, processing, analysis and presentation are no longer sufficient, which has led to the emergence of the concept of big data. However, not only the implementation of the corresponding applications is a challenging task, but also the proper quality assurance. To facilitate the latter, in this publication, a comprehensive structured literature metareview on the topic of big data quality assurance is presented. The results will provide interested researchers and practitioners with a solid foundation for their own quality assurance related endeavors and therefore help in advancing the cause of quality assurance in big data as well as the domain of big data in general. Furthermore, based on the findings of the review, worthwhile directions for future research were identified, providing prospective authors with some guidance in this complex environment.

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The 51 V's Of Big Data

Cited by 41 publications

References 14 publications

Big Data Analytics, Processing Models, Taxonomy of Tools, V’s, and Challenges: State-of-Art Review and Future Implications

Big Data Analytics, Processing Models, Taxonomy of Tools, V’s, and Challenges: State-of-Art Review and Future Implications

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Quality Assurance in Big Data Engineering - A Metareview

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