A study of big data characteristics

Kapil, Gayatri; Agrawal, Alka; Khan, Raees Ahmad

doi:10.1109/cesys.2016.7889917

Cited by 31 publications

(19 citation statements)

References 1 publication

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“…These initially comprised the 3V model [30,31]-Volume, Velocity, and Varietyacknowledged by a number of authors [32,33]. Additional Vs were added by other authors to describe big data [34,35]-Veracity, Value, and Variability [36][37][38]. Currently, the term is defined by references from 3 to 15 attributes [39].…”

Section: Big Data In Health Carementioning

confidence: 99%

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The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review (Preprint)

Mirchev¹,

Mircheva²,

Kerekovska³

2020

Preprint

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BACKGROUND Ownership of patient information in the context of Big Data is a relatively new problem, apparently not yet fully recognized by the medical academic community. The problem is interdisciplinary, incorporating legal, ethical, medical and aspects of information and communication technologies and a more sophisticated analysis of the issue is needed. However, no previous scoping review has mapped existing studies on the subject. OBJECTIVE The aim of this study is to determine how the medical academic community perceives the issue of ownership of patient information in the context of Big Data, its possible solutions and implemented practical applications. METHODS A scoping review, based on the five-stage framework outlined by Arksey and O’Malley and further developed by Levac, Colquhoun and O’Brien was conducted. The organization and the reporting of the results of the scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and its extensions for Scoping Reviews (PRISMA-ScR). A systematic and comprehensive search of four scientific information databases, PubMed, ScienceDirect, Scopus and Springer was performed for studies published between January 2000 and October 2019. Two authors independently assessed eligibility and extracted data. RESULTS The review included 32 eligible articles authored by academicians and corresponding to three focus areas: problem (ownership); area (healthcare); context (Big Data). Five major aspects were studied: scientific area of publications, aspects and academicians’ perception of ownership in the context of Big Data, proposed solutions and practical applications for data ownership issues in the context of Big Data. The aspects in which publications consider ownership of medical data are not clearly distinguished, but can be summarized as: ethical, legal, political and managerial. Ownership of patient data is perceived primarily as a challenge being fundamental to conducting medical research including data sales and sharing, and in a smaller degree as a means of control, problem, threat and opportunity also in view of primarily medical research. Although numerous solutions are falling into three categories: technology, law and policy are proposed, only three real applications are discussed. CONCLUSIONS The issue of ownership of patient information in the context of Big Data is poorly researched, is not addressed consistently and in its integrity, and there is no consensus on the ethical requirement for justice and the necessary legal regulations. What is missing is, in fact, adequate policy decisions expressed through the relevant legal framework. And in order to develop appropriate policies and regulations, ethical principles must be known, understood and upheld.

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Section: Big Data In Health Carementioning

confidence: 99%

“…Additional Vs were added by other authors to describe big data [34,35]-Veracity, Value, and Variability [36][37][38]. Currently, the term is defined by references from 3 to 15 attributes [39].…”

Section: Big Data In Health Carementioning

confidence: 99%

The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review (Preprint)

Mirchev¹,

Mircheva²,

Kerekovska³

2020

Preprint

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“…There are several studies that discuss the factors affecting the student's performance. According to [42], the performance of students has become a concern for educators. In the paper, the importance of determining the factors was highlighted as the factors are significant to analyze the performance of students.…”

Section: Related Workmentioning

confidence: 99%

“…Discussions regarding the effect of gender on study performance have dominated research in recent years. [42] have identified that the study style of male and female is different, where female tends to study in the more systematic way while male, on the other hand, tends to study at the last minute of time. In fact, [43] mentioned that from their research, most of the female students have more positive behavior compared to male.…”

Section: Related Workmentioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

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Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students' achievement, university's performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students' academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students' graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naï ve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students' graduation time.

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“…Additional Vs were added by other authors to describe big data [ 34 , 35 ]— Veracity, Value, and Variability [ 36 - 38 ]. Currently, the term is defined by references from 3 to 15 attributes [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review

Mirchev¹,

Mircheva²,

Kerekovska³

2020

J Med Internet Res

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Background The ownership of patient information in the context of big data is a relatively new problem, which is not yet fully recognized by the medical academic community. The problem is interdisciplinary, incorporating legal, ethical, medical, and aspects of information and communication technologies, requiring a sophisticated analysis. However, no previous scoping review has mapped existing studies on the subject. Objective This study aims to map and assess published studies on patient data ownership in the context of big data as viewed by the academic community. Methods A scoping review was conducted based on the 5-stage framework outlined by Arksey and O’Malley and further developed by Levac, Colquhoun, and O’Brien. The organization and reporting of results of the scoping review were conducted according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses and its extensions for Scoping Reviews). A systematic and comprehensive search of 4 scientific information databases, PubMed, ScienceDirect, Scopus, and Springer, was performed for studies published between January 2000 and October 2019. Two authors independently assessed the eligibility of the studies and the extracted data. Results The review included 32 eligible articles authored by academicians that correspond to 3 focus areas: problem (ownership), area (health care), and context (big data). Five major aspects were studied: the scientific area of publications, aspects and academicians’ perception of ownership in the context of big data, proposed solutions, and practical applications for data ownership issues in the context of big data. The aspects in which publications consider ownership of medical data are not clearly distinguished but can be summarized as ethical, legal, political, and managerial. The ownership of patient data is perceived primarily as a challenge fundamental to conducting medical research, including data sales and sharing, and to a lesser degree as a means of control, problem, threat, and opportunity also in view of medical research. Although numerous solutions falling into 3 categories, technology, law, and policy, were proposed, only 3 real applications were discussed. Conclusions The issue of ownership of patient information in the context of big data is poorly researched; it is not addressed consistently and in its integrity, and there is no consensus on policy decisions and the necessary legal regulations. Future research should investigate the issue of ownership as a core research question and not as a minor fragment among other topics. More research is needed to increase the body of knowledge regarding the development of adequate policies and relevant legal frameworks in compliance with ethical standards. The combined efforts of multidisciplinary academic teams are needed to overcome existing gaps in the perception of ownership, the aspects of ownership, and the possible solutions to patient data ownership issues in the reality of big data.

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A study of big data characteristics

Cited by 31 publications

References 1 publication

The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review (Preprint)

The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review (Preprint)

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

The Academic Viewpoint on Patient Data Ownership in the Context of Big Data: Scoping Review

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