Big Data in Health: New Challenges and New Solutions in Data Management (A Lifecycle Review)

Ehtesham, Hamideh; Safdari, Reza; Tahmasebian, Shahram

doi:10.17485/ijst/2017/v10i13/112374

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…The applicability of big data technologies [6], implemented using cloud-based infrastructures, to real-world, data-intensive scenarios has given rise to many sub-research areas. Big data has particularly found applications in healthcare [7], geospatial analytics [8] and business intelligence [9], in addition to many others. Education data is a form of big data and can make use of big data technologies for generating valuable analytics for an educational organization [10].…”

Section: Related Workmentioning

confidence: 99%

Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Khan

Alam

Liu

et al. 2019

IJACSA

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Educational Intelligence is a broad area of big data analytical applications that make use of big data technologies for implementation of solutions for education and research. This paper demonstrates the designing, development and deployment of an educational intelligence application for real-world scenarios. Firstly, a quality assessment framework for higher education systems that evaluate institutions on the basis of performance of outgoing students was proposed. Secondly, big data enabled technological setup was used for its implementation. Literature was surveyed to evaluate existing quality frameworks. Most existing quality assessment systems take into account the dimensions related to inputs, processes and outputs, but they tend to ignore the perspective that assesses the institution on the basis of outcome of the educational process. This paper demonstrates the use of outcome perspective to compute quality metrics and create visual analytics. In order to implement and test the framework, R programming language and a cloud based big data technology that is Google, BigQuery were used.

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Section: Related Workmentioning

confidence: 99%

Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Khan

Alam

Liu

et al. 2019

IJACSA

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“…Landfills and incinerators are widely used methods for disposal of biomedical waste (Marinković et al, 2008). Biomedical waste management will directly relate to potential health hazards to public health and the environment (Baguma, 2016; Ehtesham et al, 2017). Hence the focus of the review is to: execute, manage, and segregate biomedical wastes according to the classification at the point source; to review the treatments of biomedical waste as mismanagement of biomedical waste has become a global issue; and also examine the salient features of biomedical waste rules in 2016 and the future prospective of nanoparticles (NPs).…”

Section: Introductionmentioning

confidence: 99%

Nanoparticles impact in biomedical waste management

Patil

Bohara

2020

Waste Manag Res

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Effectual management of biomedical waste is obligatory for healthy human beings and for a safe environment. Mismanagement of biomedical waste is a community health problem. Safe and persistent methods for the management of biomedical waste are of vital importance. This article reviews the classification of biomedical waste, sources, colour-coding system of biomedical waste and salient features of biomedical waste rules in 2016, and the future prospective of nanoparticles. The untreated disposal of biomedical waste is associated with a huge amount of risk, so the efficient treatment for biomedical waste is most imperative. The review also highlights the current methods for disposal of biomedical waste, biological treatments given to biomedical waste water in the effluent treatment plant, and impacts due to the current method. Management of biomedical waste is a great challenge in developed and developing countries. To manage the biomedical waste there is a need for cost-effective, ecofriendly and less contaminating approaches for a greener and safe environment. The awareness regarding waste management is of great interest not only for the community but also for associated employees.

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Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques

Syed

Jabeen

Manimala

et al. 2019

Future Generation Computer Systems

133

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Big Data in Health: New Challenges and New Solutions in Data Management (A Lifecycle Review)

Cited by 3 publications

References 55 publications

Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Nanoparticles impact in biomedical waste management

Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques

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