Intelligent Data Management and Security in Cloud Computing

Ogiela, Lidia; Ogiela, Marek R.; Ko, Hoon

doi:10.3390/s20123458

Cited by 46 publications

(33 citation statements)

References 26 publications

(38 reference statements)

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“…The division of themes must ensure the independence of each theme. Data warehouse integration is the process of extracting, filtering, cleaning, and merging distributed data to meet the needs of decision analysis, to integrate the data in the database [ 11 ].…”

Section: Data Mining Technology and Economic Intelligence Systemsmentioning

confidence: 99%

Research and Application of the Data Mining Technology in Economic Intelligence System

Chang

2022

Computational Intelligence and Neuroscience

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In the context of the rapid development of the modern economy, information is particularly important in the economic field, and information determines the decision-making of enterprises. Therefore, how to quickly dig out information that is beneficial to the enterprise has become a crucial issue. This topic applies data mining technology to economic intelligent systems and obtains the data object model of economic intelligent systems through the integration of information. This article analyzes the interrelationship between its objects on this basis and uses data mining-related methods to mine it. The establishment of economic intelligence systems not only involves the establishment of mathematical models of economic systems, but also includes research on the algorithms applied to them. How to apply an algorithm to quickly and accurately extract the required economic intelligence domain information from the potential information in the database, or to provide a method to find the best solution, involves the use of association rules and classification prediction methods. The application of data mining algorithms can be used to study the application of economic intelligence systems. This paper develops and designs an economic intelligence information database and realizes the economic intelligence system on this basis, and realizes the research results. Finally, this paper has tested the dataset, and the results show that the classification accuracy of this algorithm is 2.64% higher than that of the ID3 algorithm.

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Section: Data Mining Technology and Economic Intelligence Systemsmentioning

confidence: 99%

Research and Application of the Data Mining Technology in Economic Intelligence System

Chang

2022

Computational Intelligence and Neuroscience

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“…It is difficult for students to acquire and apply science, and the outcome is not clear. It is a quick and important task to find the best psychology for students to manage, to guide them to analyze themselves [ 19 ], to understand each other more accurately, to develop themselves, to promote the health of their character [ 20 , 21 ], to maintain mental health, and to promote good behavior. Mental health education for international college students begins early.…”

Section: Introductionmentioning

confidence: 99%

Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education

Zheng

2022

Applied Bionics and Biomechanics

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With the rapid development of curricula, a large number of studies are emerging to assist in the development of curricula. But in an information society, in the face of rapid learning and increased life expectancy, students face the pressure not to forget; the mental health status as a result of our curricula is closely related to our learning. The research and application of the integration algorithm plays an important role in the analysis of the mental health education system. The purpose of this work is to study the application analysis algorithm in the students’ context. This work applies the integration analysis algorithm to students’ mental health analysis and identifies students’ mental health problems using the integration analysis algorithm so that students are well informed and guided. Based on the system engineering method, using the data mining clustering method, a detailed analysis and research on the mental health of college students is done. In this work, a method of student behavior analysis and statistical tools are used to collect mental health data to find common features of different groups of students, in order to better visualize and investigate the mental health of these students on a scientific basis. The results of this study are as follows: a general analysis algorithm application on the analysis of students’ mental health education system allows for an effective understanding of scientific data. FCM and FCM algorithms based on the density of information entropy characteristics were used to investigate the effect of mental health factors on the results of the study and the practicality of the algorithm used, which provided an effective method for the prevention of student mental problems. Assisting the school in formulating corresponding new methods of early prevention and intervention of college students’ psychological disorders will create a good and healthy atmosphere for college students’ study and life. The research results provide a reliable basis for managing and cultivating students.

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“…It is economically feasible to store, process, and update the electronic data of patients in cloud computing [26]. Graph models [27] were developed and tested to provide security in cloud computing. Cryptographic threshold techniques were applied, along with intelligent secret management and data sharing techniques.…”

Section: Related Workmentioning

confidence: 99%

Morton Filter-Based Security Mechanism for Healthcare System in Cloud Computing

Bhatia

Malhotra²

2021

Healthcare

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Electronic health records contain the patient’s sensitive information. If these data are acquired by a malicious user, it will not only cause the pilferage of the patient’s personal data but also affect the diagnosis and treatment. One of the most challenging tasks in cloud-based healthcare systems is to provide security and privacy to electronic health records. Various probabilistic data structures and watermarking techniques were used in the cloud-based healthcare systems to secure patient’s data. Most of the existing studies focus on cuckoo and bloom filters, without considering their throughputs. In this research, a novel cloud security mechanism is introduced, which supersedes the shortcomings of existing approaches. The proposed solution enhances security with methods such as fragile watermark, least significant bit replacement watermarking, class reliability factor, and Morton filters included in the formation of the security mechanism. A Morton filter is an approximate set membership data structure (ASMDS) that proves many improvements to other data structures, such as cuckoo, bloom, semi-sorting cuckoo, and rank and select quotient filters. The Morton filter improves security; it supports insertions, deletions, and lookups operations and improves their respective throughputs by 0.9× to 15.5×, 1.3× to 1.6×, and 1.3× to 2.5×, when compared to cuckoo filters. We used Hadoop version 0.20.3, and the platform was Red Hat Enterprise Linux 6; we executed five experiments, and the average of the results has been taken. The results of the simulation work show that our proposed security mechanism provides an effective solution for secure data storage in cloud-based healthcare systems, with a load factor of 0.9. Furthermore, to aid cloud security in healthcare systems, we presented the motivation, objectives, related works, major research gaps, and materials and methods; we, thus, presented and implemented a cloud security mechanism, in the form of an algorithm and a set of results and conclusions.

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Intelligent Data Management and Security in Cloud Computing

Cited by 46 publications

References 26 publications

Research and Application of the Data Mining Technology in Economic Intelligence System

Research and Application of the Data Mining Technology in Economic Intelligence System

Cluster Analysis Algorithm in the Analysis of College Students’ Mental Health Education

Morton Filter-Based Security Mechanism for Healthcare System in Cloud Computing

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