scholarly journals Service Innovation of Insurance Data Based on Cloud Computing in the Era of Big Data

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Yang ◽  
Junkai Zhou
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Customer Satisfaction ◽  
Success Rate ◽  
Insurance Industry ◽  
Service Innovation ◽  
Test Set ◽  
Test Sets ◽  
Recognition Success ◽  
Insurance Data

With the advent of the era of big data, great changes have taken place in the insurance industry, gradually entering the field of Internet insurance, and a large amount of insurance data has been accumulated. How to realize the innovation of insurance services through insurance data is crucial to the development of the insurance industry. Therefore, this paper proposes a ciphertext retrieval technology based on attribute encryption (HP-CPABKS) to realize the rapid retrieval and update of insurance data on the premise of ensuring the privacy of insurance information and puts forward an innovative insurance service based on cloud computing. The results show that 97.35% of users are successfully identified in test set A and 98.77% of users are successfully identified in test set B, and the recognition success rate of the four test sets is higher than 97.00%; when the number of challenges is 720, the modified data block is less than 9%; the total number of complaints is reduced from 1300 to 249; 99.19% of users are satisfied with the innovative insurance service; the number of the insured is increased significantly. To sum up, the insurance innovation service based on cloud computing insurance data can improve customer satisfaction, increase the number of policyholders, reduce the number of complaints, and achieve a more successful insurance service innovation. This study provides a reference for the precision marketing of insurance services.

Design and Implementation of Facial Sketch System Based on Cloud Computing Platform and Big Data

2014 ◽  
Vol 651-653 ◽  
pp. 2028-2031
Author(s):  
Yan Jun An ◽  
Yue Hou
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Success Rate ◽  
Computer Technology ◽  
Criminal Investigation ◽  
Computing Platform ◽  
Design And Implementation ◽  
Drawing System ◽  
Cloud Computing Platform ◽  
Search And Recognition

At present, the facial recognition is largely using in the criminal investigation for the identification of suspects, the information from the informants, however, is very hard to obtain the main feature and to draw it accurately because of the affection of mood, light, visual differences and so on. However, with some computer technology, combining the resource from big data and cloud computing platform, the success rate of the search and recognition could be greatly improved. Therefore this article will bring out a facial drawing system based on big data and cloud computing platform.

Configuration and Business Protocol of International Load Sharing of Manufacturing and its Challenges Under I4.0 and IIoT

2019 ◽  
Author(s):  
Samir Mekid ◽  
Usman Akbar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Customer Satisfaction ◽  
Industry 4.0 ◽  
Load Sharing ◽  
Management Condition ◽  
Investment Opportunities ◽  
Related Quality ◽  
And Performance ◽  
Manufacturing Network

Abstract The paper discusses the opportunities and challenges in a decentralized global manufacturing network infrastructure for international load sharing (ILS) envisaged for customized manufacturing. This is proposed to leverage Industry 4.0 factories to improve manufacturing throughput for worldwide orders. Smart manufactures are identified within IIoT worldwide subject to key specifications and performance. A business protocol is proposed to connect all manufacturing centers worldwide assuming their fitness to paradigm of industry 4.0 and utilizing technologies e.g. cloud computing, big-data management, condition and in-process based monitoring. The business protocol will support the ILS and manage the international orders for manufacturing products under specific requirements as a new concept compared to manual specific one. This not only allows customers to access worldwide facilities under cost, but also allows them to monitor the progress of production and related quality in real time because of IIoT. This proposed trend will lead to new investment opportunities for executives, as they will be able to sell manufacturing as a service while being connected to network from anywhere. Purchasing with online bidding, progress tracking, quick delivery and efficient routing will improve customer satisfaction.

scholarly journals Research on Service Innovation of Financial Data based on Cloud Computing in Big Data Era

2021 ◽  
Vol 13 ◽  
pp. 391-395
Author(s):  
Ting Liu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Financial Market ◽  
Value Chain ◽  
Supply And Demand ◽  
Service Innovation ◽  
Financial Data ◽  
The Internet ◽  
Chain Model ◽  
Data Value

With the increasing penetration of the Internet into people's lives, the financial market in the era of big data directly affects the asymmetric information mode between the supply and demand sides, and on this basis, it plays a positive role in guiding the subsequent optimization of financial market services, ensuring social fairness and reducing the exclusion of financial products. Big data finance under the Internet has narrowed the distance between supply and demand, subverted the problem of information asymmetry between the two sides, and injected new innovative means to alleviate financial exclusion, promote social equity and optimize financial services. In order to support the development of the real economy and enhance the core competitiveness of the industry, the process of data value chain has been continuously extended horizontally and vertically, but the data value chain of financial enterprises has been extended. In this paper, starting from the characteristics of the big data era, taking the carrier of value extension as the research goal, the big data value chain model of financial enterprises is constructed based on cloud computing technology, and the service innovation of China's financial data in the big data era is discussed.

Network Status Feedback and Implementation of Mobile Internet Based on Cloud Computing

2015 ◽  
Vol 713-715 ◽  
pp. 2280-2283
Author(s):  
Wen Juan Sun ◽  
Yi Wen Zhang ◽  
Qiao Ping Su
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Success Rate ◽  
User Experience ◽  
Setup Time ◽  
The State ◽  
Mobile Internet ◽  
Anxiety State ◽  
Network Status ◽  
Session Setup

This paper introduces a method of network status feedback and realization based on the analysis of big data. Firstly, millions of structured log of session related were converged to HDFS via Flume System. And then the data of session setup time and session success rate were acquired by Hive or Pig. The function of the two variables has been given by MATLAB. Finally, this function has been integrated in the bottom SDK. It can reflect the state of the network accurately, thus eliminating the anxiety state of users. It can well ease the tense mood of users who are waiting for the results coming from server. Obviously, this approach can improve the user experience.

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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