Multilevel Data Processing Using Parallel Algorithms for Analyzing Big Data in High-Performance Computing

Ahmad, Awais; Paul, Anand; Din, Sadia; Rathore, M. Mazhar; Choi, Gyu Sang; Jeon, Gwanggil

doi:10.1007/s10766-017-0498-x

Cited by 43 publications

(22 citation statements)

References 24 publications

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“…There is a serious information bias. In the city and large hospitals, there is still an initial construction of informatization, and the level of informatization of grassroots health institutions is not widely applied, resulting in the inversion of the hierarchical structure [21]. It is because the awareness of informatization construction of grassroots health institutions is weak, and the lack of introduction of informationized talents is lacking.…”

Section: Resultsmentioning

confidence: 99%

Research on the Secure Medical Big Data Ecosystem Based on Hadoop

Zhang

Wang

2020

Preprint

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This paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. Since traditional healing activities take place in medical institutions, patient users must travel to these institutions to learn about their treatment status. The personalized health information system designed for this purpose enables patient users to understand their treatment and rehabilitation status anytime and anywhere. The above is a consideration from the perspective of the patient user, from the perspective of personal health data. Because traditional medical health data is scattered throughout different independent medical institutions, and these databases are heterogeneous. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. The characteristics of blockchain without a central server make the system without a single point In case of failure, the stability of the system is well maintained. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

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Section: Resultsmentioning

confidence: 99%

Research on the Secure Medical Big Data Ecosystem Based on Hadoop

Zhang

Wang

2020

Preprint

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“…Among them, the national e-health system project budget of 6.1 billion yuan, including the implementation of the standardization of large-scale integrated hospital information system, the establishment of a national electronic health record and the creation of a regional medical information platform. This is the rst time the government has clearly stated the scale of investment in the direction of medical informatization, indicating that the status of informatization in the reform of the medical system is constantly improving [21].…”

Section: Related Workmentioning

confidence: 96%

Research on intelligent medical big data system based on Hadoop and blockchain

Zhang

Wang

2020

Preprint

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In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data Distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

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“…The development of IoA devices and systems with processors, sensors, and actuators will require the further development and integration of several enabling technologies, e.g. Augmented and Virtual Reality (AR/VR) [54,55,123], High Performance Computing [124], Cloud and Fog Computing [125][126][127], Cyber-Physical Production Systems [128], Big Data Analytics [129], ultrafast communication infrastructures and standards, artificial intelligence [130], data storage, sensors and monitoring [131], wearable devices [68], and actuator technologies.…”

Section: Industrial and Technological Challengesmentioning

confidence: 99%

Key Research Priorities for Factories of the Future—Part I: Missions

Tolio

Copani

Terkaj

2019

Factories of the Future

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This chapter investigates research priorities for factories of the future by adopting an approach based on mission-oriented policies to support manufacturing innovation. Missions are challenging from a scientific and technological point of view and, at the same time, are addressing problems and providing results that are understandable by common people. Missions are based on clear targets that can help mitigating grand challenges. Based on the results of the Italian Flagship Project Factories of the Future, this chapter proposes seven missions while identifying the societal impact, the technological and industrial challenges, and the barriers to be overcome. These missions cover topics such as circular economy, rapid and sustainable industrialisation, robotic assistant, factories for personalised medicine, internet of actions, factories close to the people, and turning ideas into products. The accomplishment of missions asks for the support of a proper research environment in terms of infrastructures to test and demonstrate the results to a wide public. Research infrastructures together with funding mechanisms will be better addressed in the next chapter of this book. 20.1 Mission-Oriented Research and Innovation Policies The importance of the manufacturing industry both for developed and developing countries has been assessed in several works [1-4], which highlight the need of continuous innovation to cope with societal grand challenges [5]. The results of inno-T. Tolio Director of the Italian Flagship Project "Factories of the Future", Direttore del Progetto Bandiera "La Fabbrica del Futuro",

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Multilevel Data Processing Using Parallel Algorithms for Analyzing Big Data in High-Performance Computing

Cited by 43 publications

References 24 publications

Research on the Secure Medical Big Data Ecosystem Based on Hadoop

Research on the Secure Medical Big Data Ecosystem Based on Hadoop

Research on intelligent medical big data system based on Hadoop and blockchain

Key Research Priorities for Factories of the Future—Part I: Missions

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