”The Architectural Design of Healthcare Service with Big-Data Storing Mechanism”, Md. Salah Uddin, Shaikh Muhammad Allayear and Sung Soon Park, e-ISSN: 2278-0661,p- ISSN: 2278-8727, Volume 16, Issue 5, Ver. VIII (Sep – Oct. 2014), PP 81-94 www.iosrjournals.org (original) (raw)
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The Architectural Design of Healthcare Service with Big-Data Storing Mechanism
Abstract: Healthcare is the diagnosis, treatment, and prevention of disease, illness, injury, and other physical and mental impairments in human beings. Healthcare is delivered by practitioners in allied health, dentistry, midwifery-obstetrics, medicine, nursing, optometry, pharmacy, psychology and other care providers. In healthcare service to provide better performance to identify some diseases like blood pressure, skin cancer, diabetes etc. with the sensor devices those are connected with home server by using wireless sensor or wired network policies. The size of data sets being collected and analyzed in the industry for healthcare business intelligence is growing rapidly. In healthcare service case we have to concern about massive data services. To store and process huge amount of unstructured data by using traditional database system is so difficult. To alleviate this drawback we can use Hadoop architecture that has functionality to store and process huge amount of unstructured data. Hadoop Distributed file system (HDFS) can store and the Hadoop MapReduce framework process huge amount of unstructured data that enables easy development of scalable parallel applications. This paper’s main motivation is to ensure better data transmission, data reliability and massive data processing with High Availability (HA) based network storage solution. So, in this paper we proposed MapReduce Agent (MRA) to process Big-Data and also proposed iSCSI Protocol adapted Network Attached Storage (NAS) system for healthcare service. Keywords: HealthCare Service, Big-Data, Hadoop, Sensor Integration and iSCSI.
Healthcare Big Data Analysis using Hadoop MapReduce
International Journal of Scientific and Research Publications (IJSRP), 2019
The large volumes of healthcare big data are rapidly generating all over the world in numerous ways. Therefore, significant amount of money has been allocating for healthcare industry for treatments, diagnosis and other research and development areas in handling healthcare big data. Further, patients are unnecessarily spending time, effort and money, due to lack of telemedicine support. However, the rapid growth of unstructured healthcare data does not support for existing big data analyzing technologies. Therefore, the study suggests Hadoop MapReduce for store and process medical data to avoid the modern issues in healthcare big data analysis.
IJMTST, 2019
In today's world, due to technological advancements, the amount of data that is getting generated is growing rapidly. Enterprises worldwide will need to perform data analytics with these huge data datasets to make business decisions and stay competitive. Storage of data sets and performing data analytics was traditionally accomplished using RDBMS (Relational Database Management System). However, RDBMS would be inefficient and time consuming when performing data analytics on huge data sets. A cloud based big data analytic platform is the best way to analyze the structured and unstructured data generated from healthcare management systems. Hadoop came into existence recently and overcomes the limitations of existing RDBMS by providing simplified tools for efficient data storage and faster processing times for data analytics. The purpose of this work is to perform data analytics on a health care data set using Hadoop functionalities. A health care data set comprising of 1.5 million patient records is considered for the data analysis. Different use cases as to be considered and will perform analytics using MapReduce, Hive and Pig functionalities of Hadoop.
Procedia Computer Science, 2016
The critical challenge that the healthcare organizations are facing is to analyze the large-scale data. With the rapid growth of various healthcare applications, various devices used in healthcare generate varieties of data. The data need to be processed and effectively analyzed for better decision making. Cloud computing is a promising technology which can provide ondemand services for storage, processing and analyzing the data. The traditional data processing systems no longer has an ability to process such huge data. In order to achieve a better performance and to solve the scalability issues we need a better distributed system on cloud environment. Hadoop is a framework which can process large scale data sets on distributed environment. Hadoop can be deployed on cloud environment to process the large scale healthcare data. Healthcare applications are being supplied through internet and cloud services rather than using as traditional software. Healthcare providers need to have real time information to provide quality healthcare.This paper discuss on the impacts of data processing and analyzing large scale healthcare data on cloud computing environment.
Health care organizations now a day’s made a strategic decision to turn huge medical data coming from various sources into competitive advantage. This will help the health care organizations to monitor any abnormal measurements which require immediate reaction. Apache Hadoop has emerged as a software framework for distributed processing of large datasets across large clusters of computers. Hadoop is based on simple programming model called MapReduce. Hive is a data warehousing framework built on top of hadoop. Hive is designed to enable easy data summarization, ad-hoc querying and analysis of large volume of data. As health care and Electronic Medical Records (EMR) are generating huge data, it is necessary to store, extract and load such big data using a framework which support distributed processing. Cloud computing model provides efficient resources to store and process the data. In this paper we propose a MapReduce programming for Hadoop which can analyze the EMR on cloud. Hive is used to analyze large data of healthcare and medical records. Sqoop is used for easy data import and export of data from structured data stores such as relational databases, enterprise datawarehouses and NoSQL systems.
Creating a Health Data Management Platform using Hadoop
CERN European Organization for Nuclear Research - Zenodo, 2022
Customary, conventional healthcare Database Management Systems are used as a repository of data and to process structured data efficiently, but in case of diverse variety and huge volumes of data it becomes arduous to handle such mammoth volumes. The question arises of what and how to process such data from various sources which could be structured as well as unstructured and in a distributed manner? Hadoop is open source framework, based on distributed computing, which is capable of storing and processing Big Data, which may comprise of structured, unstructured as well as semi-structured data. In this paper, we summarize the basic operations performed on healthcare data in a Data Management Lifecycle.
—Over the years with automation more and more systems deployed in multiple industries are generating huge amount of data. In fact IT Industry itself has witnessed phenomenal growth of data in the recent years. The data generated in the last 5 years is much more then the data generated cumulatively by all the industries put together in the past 20 years. In the current work we focus on the ways and means to handle the data generated by PHIS(Personal Healthcare Information System). The big question which we have addressed in this paper is selection of the appropriate tool (Relational MySQL database or NoSQL MongoDB database) to store the patient data, its archival and storage, steps to mine it and concluded the work by depicting the comparative analysis in terms of space and time.
Cloud Data Management based on Hadoop Framework
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Cloud computing is joined with a new model for supplying of computing infrastructure. Big Data management has been specified as one of the momentous technologies for the next years. This paper shows a comprehensive survey of different approaches of data management applications using MapReduce. The open source framework implementing the MapReduce algorithm is Hadoop. We simulate the different design examples of the MapReduce which stored on the cloud. This paper proposes the application of MapReduce which runs on a huge cluster of machines, in Hadoop framework. The proposed implantation methodology is highly scalable and easy to use for non professional users. The main objective is to improve the performance of the MapReduce data management system in the basis of the Hadoop framework. Simulation result shows the effectiveness of the proposed implementation methodology for the MapReduce.
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Organizing and maintaining the big data are the two major concerns which have led to many challenges for the organization. The main objective of this research work is to give an overall idea about organizing Big data with High performance. MapReduce is one of the commonly used techniques which is used to analyze a large volume of data in an efficient manner. A common overview of Big data and the implementation of the MapReducing technique on Biomedical Big Data has been discussed in this paper with an algorithm. Discussion on performance analysis of MapReducing technique being will open the doors for further research activities in Big Data Analytics and MapReducing technique. Highlight of this research work is the data which has been selected and the output of the research work has been openly discussed to help the beginners of Big data. The proposed research work will give an insight about the implementation of Hadoop Distributed File System for small and medium sized business.
Hadoop: Addressing challenges of Big Data
2014 IEEE International Advance Computing Conference (IACC), 2014
Hadoop is an open source cloud computing platform of the Apache Foundation that provides a software programming framework called MapReduce and distributed file system, HDFS. It is a Linux based set of tools that uses commodity hardware, which are relatively inexpensive, to handle, analyze and transform large quantity of data. Hadoop Distributed File System, HDFS, stores huge data set reliably and streams it to user application at high bandwidth and MapReduce is a framework that is used for processing massive data sets in a distributed fashion over a several machines. This paper gives a brief overview of Big Data, Hadoop MapReduce and Hadoop Distributed File System along with its architecture.