m-cloud -- Distributed Statistical Computation Using Multiple Cloud Computers

Nakagawa, Ikuo; Hiji, Masahiro; Kikuchi, Yutaka; Fukumoto, Masahiro; Shimojo, Shinsuke

doi:10.1109/compsacw.2014.53

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…First, cloud computing in health care mainly focuses on saving costs and improving computing efficiency. Some scholars have applied distributed storage and computing to health care systems, demonstrating that cloud computing can enhance the performance of health care systems and increase the satisfaction with smart health care applications and services [43,64,65]. The main issues of cloud computing are focused on distributed storage algorithms, resource-indexing techniques in distributed storage, Hadoop-based distributed storage and computing applications, and distributed computing models.…”

Section: Discussionmentioning

confidence: 99%

“…More specifically, scholars have proposed algorithms and architectures to address efficiency and security issues. Nakagawa et al [43] proposed m-cloud, a distributed computing mechanism with multiple cloud resources. It not only increases resiliency, scalability, and efficiency but also reduces the risk of revealing private information.…”

Section: Co-word Analysis For Phase III (2013-2015)mentioning

confidence: 99%

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Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis

Gu¹,

Yang²,

Deng³

et al. 2020

J Med Internet Res

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Background With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars in the medical informatics community need to understand the extent of the evolution of and possible trends in cloud health care research to inform their future research. Objective Drawing on the cloud health care literature, this study aimed to describe the development and evolution of research themes in cloud health care through a knowledge map and common word analysis. Methods A total of 2878 articles about cloud health care was retrieved from the Web of Science database. We used cybermetrics to analyze and visualize the keywords in these articles. We created a knowledge map to show the evolution of cloud health care research. We used co-word analysis to identify the hotspots and their evolution in cloud health care research. Results The evolution and development of cloud health care services are described. In 2007-2009 (Phase I), most scholars used cloud computing in the medical field mainly to reduce costs, and grid computing and cloud computing were the primary technologies. In 2010-2012 (Phase II), the security of cloud systems became of interest to scholars. In 2013-2015 (Phase III), medical informatization enabled big data for health services. In 2016-2017 (Phase IV), machine learning and mobile technologies were introduced to the medical field. Conclusions Cloud health care research has been rapidly developing worldwide, and technologies used in cloud health research are simultaneously diverging and becoming smarter. Cloud–based mobile health, cloud–based smart health, and the security of cloud health data and systems are three possible trends in the future development of the cloud health care field.

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Section: Discussionmentioning

confidence: 99%

Section: Co-word Analysis For Phase III (2013-2015)mentioning

confidence: 99%

Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis

Gu¹,

Yang²,

Deng³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…The authors conclude that the underlying challenges have to be further investigated. In a study by Nakagawa et al, the authors proposed an approach named m-cloud [49]. The approach aids in collecting sensor data using cloud resources for IoT data.…”

Section: B Rq2:what Are the Contributions Been Proposed By The Selecmentioning

confidence: 99%

Data Collection in Sensor-Cloud: A Systematic Literature Review

et al. 2020

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The integration of cloud computing and Wireless Sensor Networks (WSNs) to create Sensorcloud helps in extending the data processing capability and storage capability of WSNs. Knowing how weak WSNs are with regards to communication ability, how to collect and upload sensory data to the cloud in limited time has become an issue in Sensor-cloud. In the last decade, with increasing interest by researchers in the domain, a considerable amount of research works have been conducted and published in the research domain. The main objective of this study is to systematically review the current research on data collection in Sensor-cloud. Hence, the study also aims at identifying, categorizing, and synthesizing important studies in the field of study. Accordingly, an evidence-based methodology is utilized in this study. By doing so,43 relevant studies were identified and retrieved to answer the formulated research questions. The systematic methodology offers a methodical and rigorous study selection and evaluation process that is repeatable and precise. The result shows that research on data collection in Sensor-cloud is relatively consistent with stable output in the last five years. Ten proposal contributions were identified with System, Framework, and Algorithm being the most used by the selected studies. In conclusion, key research challenges and future research directions were identified and discussed for researchers to propose effective solutions to the existing challenges. Although research on data collection in Sensor-cloud is gaining some traction in recent years, the works in the domain are not sufficient and concrete proposals are needed to improve data collection.

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“…The authors conclude that the underlying challenges have to be further investigated. In a study by Nakagawa et al, the authors proposed an approach named m-cloud [48]. The approach aids in collecting sensor data using cloud resources for IoT data.…”

Section: B Rq2:what Are the Contributions Been Proposed By The Selecmentioning

confidence: 99%