Research on the Management Model of Marine Economic Information Resources in the Big Data Era Based on Cloud Computing

2019 ◽  
Vol 94 (sp1) ◽  
pp. 316
Author(s):  
Yanan Zhao ◽  
Hongwei Zhao
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Information Resources ◽  
Management Model ◽  
Economic Information

scholarly journals Research on Innovation of Financial Management Model Based on Cloud Computing

2020 ◽  
Vol 214 ◽  
pp. 03037
Author(s):  
Song yang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Resource Sharing ◽  
Financial Management ◽  
Service Providers ◽  
Management Model ◽  
Dramatic Improvement ◽  
Management Models ◽  
Cloud Computing Service ◽  
Management Services

Against the background of the rapid development of big data and big data analysis technologies, cloud computing service providers provide services to enterprises and individuals. In many large enterprises, by setting up a cloud computing platform environment, multiple services within the enterprise are placed on the cloud platform to provide resource sharing services for various departments. With the dramatic improvement in computing performance of computer clusters, big data and cloud computing technologies are maturing. We combine cloud computing with financial management services, and use modern technical means to innovate financial management models; we will change the financial management model of enterprises by building financial sharing service centers, thereby providing more efficient financial management services. Based on the research of cloud computing technology, this paper expounds the application of cloud computing in financial sharing, and explores the transformation of financial management models in the context of cloud computing. Fully clarify the principles and procedures for the construction of financial sharing service centers based on cloud computing, and discuss the basic structure of financial cloud management. Finally, we discuss the operation model of financial management based on cloud computing to provide a reference for researchers in financial management.

Infoeconomics

2020 ◽  
pp. 306-326
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Organizational Performance ◽  
Balanced Scorecard ◽  
Process Management ◽  
Management Model ◽  
Costs And Benefits ◽  
Productivity Paradox ◽  
It Productivity Paradox ◽  
It Productivity

The chapter discusses economics of informing or infoeconomics, which refers to costs and benefits of informing agents and to their contribution to organizational performance. Controversies questioning contributions of IT/IS to productivity (IT productivity paradox) and to competitiveness (IT commodification argument) are discussed. Several methods of assessing costs and benefits of informing agents are proposed. Assessing benefits from IT/IS, data, and knowledge is challenging since their impact is usually not immediately visible. The challenges have become more pressing with the data analytics and big data trends. Several research cases are used to demonstrate relationships between IVO aspects and infoeconomics. It is argued that a business process management model and balanced scorecard methodology are reliable guides for study and management of infoeconomics. The implications of big data and cloud computing on infoeconomics are discussed throughout the chapter.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

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