Fifty-Six Big Data V’s Characteristics and Proposed Strategies to Overcome Security and Privacy Challenges (BD2)

Hussein, Abou_el_ela Abdou

doi:10.4236/jis.2020.114019

Cited by 16 publications

(12 citation statements)

References 32 publications

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“…Their research provides valuable insights into the challenges of big data management in healthcare, highlighting the importance of strategic planning and prioritization in addressing these obstacles. Hussein (2020) discusses the security and privacy challenges associated with big data, proposing technological solutions to address these issues. The study emphasizes the need for new strategies to manage the security and privacy concerns inherent in big data analytics, highlighting the evolving nature of these challenges as data volumes and complexity continue to grow.…”

Section: Challenges In Adopting Big Data Strategiesmentioning

confidence: 99%

Big data applications in portfolio management: A review of techniques and strategies

Olubusola Odeyemi,

Noluthando Zamanjomane Mhlongo,

Ekene Ezinwa Nwankwo

et al. 2024

Int. J. Sci. Res. Arch.

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In the ever-evolving landscape of finance, the advent of big data has ushered in a paradigm shift, transforming the art and science of portfolio management from its traditional moorings to a future where data reigns supreme. This paper delves into the intricate dance between big data and portfolio management, aiming to demystify the journey from historical practices to the forefront of technological innovation in finance. Through a meticulous examination, the study sets out to map the evolution of portfolio management in the digital age, scrutinize the technological foundations essential for big data analytics, and explore the symbiotic relationship between big data capabilities and investment strategies. Employing a systematic literature review as its methodological backbone, the research navigates through the complexities of integrating big data into portfolio management, offering a panoramic view of the current landscape and the challenges that lie ahead. The findings illuminate the transformative impact of big data analytics, highlighting its potential to enhance decision-making, optimize risk assessment, and unearth new investment opportunities. However, the journey is fraught with challenges, including data privacy concerns, ethical considerations, and the need for continuous technological adaptation. In conclusion, the study advocates for a proactive embrace of big data, urging portfolio managers to cultivate a culture of innovation, ethical vigilance, and lifelong learning. As the financial sector stands on the brink of a data-driven era, this paper calls for a strategic reorientation towards the integration of big data analytics in portfolio management, paving the way for a future where informed decision-making and strategic foresight are paramount.

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Section: Challenges In Adopting Big Data Strategiesmentioning

confidence: 99%

Big data applications in portfolio management: A review of techniques and strategies

Olubusola Odeyemi,

Noluthando Zamanjomane Mhlongo,

Ekene Ezinwa Nwankwo

et al. 2024

Int. J. Sci. Res. Arch.

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“…Volume alludes to the monstrous measure of information produced each second and applies to the size and size of a dataset. It is unreasonable to characterize a general limit for enormous information volume (i.e., what comprises a 'major dataset') in light of the fact that the time and sort of information can impact its definition [5].…”

Section: Figure 1 5 V's Of Big Datamentioning

confidence: 99%

Handling Uncertainty, Noise and Fake News Problem in Big Data

Singh

2022

J. Appl. Sci. Educ.

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A tremendous store of terabytes of information is produced every day from present-day data frameworks and computerized innovations. Examination of this monstrous information requires plenty of endeavors at different levels to separate information for dynamic. In a computerized world, information is created from different sources and the quick progress from advanced advances has prompted the development of Big Data. It furnishes a transformative leap forward in many fields with an assortment of enormous datasets. As a rule, it alludes to the assortment of enormous and complex datasets which are hard to handle utilizing conventional data set administration apparatuses or information handling applications. The majority of the introduced approaches in information mining are not normally ready to deal with the enormous datasets effectively. The critical issue in the examination of Big Data is the absence of coordination between information base frameworks just as with investigation instruments like information mining and factual examination. These difficulties by and large emerge when we wish to perform information disclosure and portrayal for its viable applications. A crucial issue is how to quantitatively portray the fundamental qualities of Big Data. There is a requirement for epistemological ramifications in portraying information upheaval. Also, the review on the intricacy hypothesis of Big Data will assist with understanding fundamental attributes and arrangement of complicated examples in Big Data, improve its portrayal, improve information reflection, and guide the plan of registering models and calculations on Big Data.

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“…To put it another way, the incoming data are worthwhile and suitable for big data analysis. The implementation of data quality management principles differs slightly from the application of normal data due to additional technological and organisational obstacles brought about by big data [ 31 ]. Some of these facts are gathered in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Data quality model for assessing public COVID-19 big datasets

et al. 2023

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For decision-making support and evidence based on healthcare, high quality data are crucial, particularly if the emphasized knowledge is lacking. For public health practitioners and researchers, the reporting of COVID-19 data need to be accurate and easily available. Each nation has a system in place for reporting COVID-19 data, albeit these systems’ efficacy has not been thoroughly evaluated. However, the current COVID-19 pandemic has shown widespread flaws in data quality. We propose a data quality model (canonical data model, four adequacy levels, and Benford’s law) to assess the quality issue of COVID-19 data reporting carried out by the World Health Organization (WHO) in the six Central African Economic and Monitory Community (CEMAC) region countries between March 6,2020, and June 22, 2022, and suggest potential solutions. These levels of data quality sufficiency can be interpreted as dependability indicators and sufficiency of Big Dataset inspection. This model effectively identified the quality of the entry data for big dataset analytics. The future development of this model requires scholars and institutions from all sectors to deepen their understanding of its core concepts, improve integration with other data processing technologies, and broaden the scope of its applications.

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Fifty-Six Big Data V’s Characteristics and Proposed Strategies to Overcome Security and Privacy Challenges (BD2)

Cited by 16 publications

References 32 publications

Big data applications in portfolio management: A review of techniques and strategies

Big data applications in portfolio management: A review of techniques and strategies

Handling Uncertainty, Noise and Fake News Problem in Big Data

Data quality model for assessing public COVID-19 big datasets

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