Developing a File System Structure to Solve Healthy Big Data Storage and Archiving Problems Using a Distributed File System

Ergüzen, Atilla; Ünver, Mahmut

doi:10.3390/app8060913

Cited by 20 publications

(10 citation statements)

References 23 publications

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“…As in other fields, the volume of data produced every day is also increasing exponentially in the area of modern health (Ergüzen and Ünver 2018 ). Allied to this scenario, and with the development of smart devices and Cloud Computing, a huge amount data coming from public health occurences can be collected from various sources and analyzed in different ways.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Corsi

Souza

Pagani

et al. 2020

J Ambient Intell Human Comput

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Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Corsi

Souza

Pagani

et al. 2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…The table SERVICES is mapped as ′ ′ where the attribute 'preservice': (5), and 'currservice': (6) are a constant value of varchar datatype for a patient registered or admitted under different clinical services. The attributes (5) , and (6) are the previous and the current clinical services, respectively, under which the patient was admitted.…”

Section: Services ( ı )mentioning

confidence: 99%

“…One of the critical requirements from the hospital management perspective and patient's well-being is predicting random readmission due to the risk of complexities after providing specific procedures and discharge [15]. Advanced healthcare data acquisition technologies acquire various formats of data from multiple dynamic sources over a timeline [3,7,20] and a distributed file system-oriented data-models support the intrinsic characteristics like heterogeneity, uncertainty, and high dimensionality of these data [5,9]. There is a growing trend in data processing and artificial intelligence to build reporting and billing applications in the healthcare industry using these data [2,26].…”

Section: Introductionmentioning

confidence: 99%

Critical Correlation of Predictors for an Efficient Risk Prediction Framework of ICU Patient Using Correlation and Transformation of MIMIC-III Dataset

Khope

Elias

2022

Data Sci. Eng.

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Various predictive frameworks have evolved over the last decade to facilitate the efficient diagnosis of critical diseases in the healthcare sector. Some have been commercialized, while others are still in the research and development stage. An effective early predictive principle must provide more accurate outcomes in complex clinical data and various challenging environments. The open-source database system medical information mart for intensive care (MIMIC) simplifies all of the attributes required in predictive analysis in this regard. This database contains clinical and non-clinical information on a patient’s stay at a healthcare facility, gathered during their duration of stay. Regardless of the number of focused research attempts employing the MIMIC III database, a simplified and cost-effective computational technique for developing the early analysis of critical problems has not yet been found. As a result, the proposed study provides a novel and cost-effective machine learning framework that evolves into a novel feature engineering methodology using the MIMIC III dataset. The core idea is to forecast the risk associated with a patient’s clinical outcome. The proposed study focused on the diagnosis and clinical procedures and found distinct variants of independent predictors from the MIMIC III database and ICD-9 code. The proposed logic is scripted in Python, and the outcomes of three common machine learning schemes, namely Artificial Neural Networks, K-Nearest Neighbors, and Logistic Regression, have been evaluated. Artificial Neural Networks outperform alternative machine learning techniques when accuracy is taken into account as the primary performance parameter over the MIMIC III dataset.

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“…As the use of big data systems increases, new technologies are emerging based on big data, and existing technologies are becoming more advanced, increasing the complexity of system architectures. Such advanced technologies such as distributed file systems [5], non-relational SQL [6], cloud systems, machine learning [7], security, etc., are considered part of the big data system. For example, Ge et al [8] suggested that IoT operated in the big data lifecycle and provided an efficient operation plan in the big data system.…”

Section: Introductionmentioning

confidence: 99%

C4I System Security Architecture: A Perspective on Big Data Lifecycle in a Military Environment

Baek

Kim

2021

Sustainability

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Although the defense field is also one of the key areas that use big data for security reasons, there is a lack of study that designs system frameworks and presents security requirements to implement big data in defense. However, we overcome the security matters by examining the battlefield environment and the system through the flow of data in the battlefield. As such, this research was conducted to apply big data in the defense domain, which is a unique field. In particular, a three-layered system framework was designed to apply big data in the C4I system, which collects, manages, and analyzes data generated from the battlefield, and the security measures required for each layer were developed. First, to enhance the general understanding of big data and the military environment, an overview of the C4I system, the characteristics of the 6V’s, and the five-phase big data lifecycle were described. While presenting a framework that divides the C4I system into three layers, the roles and components of each layer are described in detail, considering the big data lifecycle and system framework. A security architecture is finally proposed by specifying security requirements for each field in the three-layered C4I system. The proposed system framework and security architecture more accurately explain the unique nature of the military domain than those studied in healthcare, smart grids, and smart cities; development directions requiring further research are described.

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Developing a File System Structure to Solve Healthy Big Data Storage and Archiving Problems Using a Distributed File System

Cited by 20 publications

References 23 publications

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Critical Correlation of Predictors for an Efficient Risk Prediction Framework of ICU Patient Using Correlation and Transformation of MIMIC-III Dataset

C4I System Security Architecture: A Perspective on Big Data Lifecycle in a Military Environment

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