Optimal Operational Definition of Patient with Peptic Ulcer Bleeding for Big Data Analysis Using Combination of Clinical Characteristics in a Secondary General Hospital

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

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An overview of big data analysis

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i4.2359 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1646-1653

Author(s):

Fabio Arena ◽

Giovanni Pau

Keyword(s):

Big Data ◽

Data Analysis ◽

Customer Service ◽

Big Data Analytics ◽

Short Review ◽

Big Data Analysis ◽

Digital World ◽

Big Data Technologies ◽

Definition Of ◽

Smart Cars

Big data represents one of the most profound and most pervasive evolutions in the digital world. Examples of big data come from Internet of Things (IoT) devices, as well as smart cars, but also the use of social networks, industries, and so on. The sources of data are numerous and continuously increasing, and, therefore, what characterizes big data is not only the volume but also the complexity due to the heterogeneity of information that can be obtained. The fastest growth in spending on big data technologies is happening within banking, healthcare, insurance, securities and investment services, and telecommunications. Remarkably, three of those industries lie within the financial sector, which has many particularly serviceable use cases for big data analytics, such as fraud detection, risk management, and customer service optimization. In fact, the definition of big data analysis refers to the process that encompasses the gathering and analysis of big data to obtain useful information for the business. This paper focuses on delivering a short review concerning the current technologies, future perspectives, and the evaluation of some use cased associated with the analysis of big data.

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Sa1023 - The Trend of Peptic Ulcer Bleeding Incidence over the Past 10 Years in Korean National Health Insuarance Service Big Data: the Effect of Gender and Economic Status

Gastroenterology ◽

10.1016/s0016-5085(18)31150-8 ◽

2018 ◽

Vol 154 (6) ◽

pp. S-229

Author(s):

Yong Sung Kim ◽

Suck Chei Choi ◽

Jong Heon Park ◽

Joon Ki Lee ◽

Aesun Shin ◽

...

Keyword(s):

Big Data ◽

Peptic Ulcer ◽

National Health ◽

Economic Status ◽

Ulcer Bleeding ◽

Peptic Ulcer Bleeding ◽

The Past

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Big Data Analysis and Implementation in Different Areas Using IoT

Securing the Internet of Things ◽

10.4018/978-1-5225-9866-4.ch049 ◽

2020 ◽

pp. 1096-1111

Author(s):

Aqeel ur Rehman ◽

Muhammad Fahad ◽

Rafi Ullah ◽

Faisal Abdullah

Keyword(s):

Data Mining ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Healthcare Applications ◽

Standard Data ◽

Huge Data ◽

Data Rates ◽

Definition Of ◽

Huge Challenge

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

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Comparison of Clinical Characteristics and Outcomes between Geriatric and Non-geriatric Patients in Peptic Ulcer Bleeding

Korean Journal of Gastroenterology ◽

10.4166/kjg.2009.53.5.297 ◽

2009 ◽

Vol 53 (5) ◽

pp. 297 ◽

Cited By ~ 2

Author(s):

Youn Ju Na ◽

Ki-Nam Shim ◽

Min Jung Kang ◽

Ji Min Jung ◽

Seong-Eun Kim ◽

...

Keyword(s):

Peptic Ulcer ◽

Clinical Characteristics ◽

Geriatric Patients ◽

Ulcer Bleeding ◽

Peptic Ulcer Bleeding

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Big Data from A to Z

Frontiers in Health Informatics ◽

10.30699/fhi.v8i1.202 ◽

2019 ◽

Vol 8 (1) ◽

pp. 20

Author(s):

Elham Nazari ◽

Marziyeh Afkanpour ◽

Hamed Tabesh

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Science ◽

Rapid Development ◽

Big Data Analysis ◽

Data Sources ◽

The Past ◽

Logical Time ◽

Definition Of

The rapid development of technology over the past 20 years has led to explosive data growth in various industries, including defense industries, healthcare. The analysis of generated Big Data has recently been addressed by many researchers, because today's Big Data analysis are one of the most important and most profitable areas of development in Data Science and companies that are able to extract valuable knowledge among the massive amount of data at logical time can earn significant advantages . Accordingly, in this survey, we investigate definition of the Big Data and the data sources. Also look at advantages, challenges, applications, analysis and platforms used in the Big Data.

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