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big data Access Control Mechanism Based on Two-Layer Permission Decision Structure

Computers, Materials & Continua 2024年第4期79卷 1705-1726页

作者： Aodi Liu Na Wang Xuehui Du Dibin Shan Xiangyu Wu Wenjuan Wang He’nan Province Key Laboratory of Information Security Information Engineering UniversityZhengzhou450000China

big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business constraints.The BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

关键词： big data access control data security BiLSTM

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big data challenge for monitoring quality in higher education institutions using business intelligence dashboards

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Journal of Electronic Science and Technology 2024年第1期22卷 25-41页

作者： Ali Sorour Anthony S.Atkins Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom

As big data becomes an apparent challenge to handle when building a business intelligence(BI)system,there is a motivation to handle this challenging issue in higher education institutions(HEIs).Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources.This paper reviews big data and analyses the cases from the literature regarding quality assurance(QA)in HEIs.It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper.The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data.The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’QA systems.This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.

关键词： big data Business intelligence(BI) Dashboards Higher education(HE) Quality assurance(QA) Social media

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Reliability evaluation of IGBT power module on electric vehicle using big data

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Journal of Semiconductors 2024年第5期45卷 50-60页

作者： Li Liu Lei Tang Huaping Jiang Fanyi Wei Zonghua Li Changhong Du Qianlei Peng Guocheng Lu The School of Electrical Engineering Chongqing UniversityChongqing 400044China The Deepal Automobile Technology Co. Ltd.Chongqing 400023China

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

关键词： IGBT junction temperature neural network electric vehicles big data

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An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve data Availability in the Context of big data

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Computers, Materials & Continua 2024年第4期79卷 1561-1579页

作者： Linlin Yuan Tiantian Zhang Yuling Chen Yuxiang Yang Huang Li State Key Laboratory of Public big data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China College of Information Engineering Guizhou Open UniversityGuiyang550025China Guizhou Academy of Tobacco Science Guiyang550025China

The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more prominent.The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big data.However,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability.In addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced.Based on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data usability.Specifically,we construct a new information loss function based on the information quantity theory.Considering that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss function.In addition,to reduce information loss,we improve K-anonymity in two ways.First,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering algorithm.In addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass.Meanwhile,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information loss.Finally,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.

关键词： Blockchain big data K-anonymity 2-means clustering greedy algorithm mean-center method

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Extracting Integrated Features of Electronic Medical Records big data for Mortality and Phenotype Prediction

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Chinese Journal of Electronics 2024年第3期33卷 776-792页

作者： Fei LI Yiqiang CHEN Yang GU Yaowei WANG Institute of Computing Technology University of Chinese Academy of Sciences Pengcheng Laboratory

The key to synthesizing the features of electronic medical records(EMR) big data and using them for specific medical purposes,such as mortality and phenotype prediction,is to integrate the individual medical event and the overall multivariate time series feature extraction automatically,as well as to alleviate data imbalance problems.This paper provides a general feature extraction method to reduce manual intervention and automatically process large-scale data.The processing uses two variational auto-encoders(VAEs) to automatically extract individual and global features.It avoids the well-known posterior collapse problem of Transformer VAE through a uniquely designed "proportional and stabilizing" mechanism and forms a unique means to alleviate the data imbalance problem.We conducted experiments using ICU-STAY patients' data from the MIMIC-Ⅲ database and compared them with the mainstream EMR time series processing methods.The results show that the method extracts visible and comprehensive features,alleviates data imbalance problems and improves the accuracy in specific predicting tasks.

关键词： databases Time series analysis MIMICs Manuals big data Feature extraction Transformers

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Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

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The Crop Journal 2024年第3期12卷 803-813页

作者： Moritz Lell Yusheng Zhao Jochen C.Reif Leibniz Institute of Plant Genetics and Crop Plant Research(IPK) Corrensstraße 3D-06466 SeelandGermany

Genome-wide association mapping studies(GWAS)based on big data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to big data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.

关键词： big data Genome-wide association study data integration Genomic prediction Wheat

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Research on Tax Risk Regulation and Strategic Management in the Context of big data

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Proceedings of Business and Economic Studies 2024年第2期7卷 145-150页

作者： Shouzeng Gong Shanghai Customs College Shanghai 201204China

With the rapid development of big data,big data has been more and more applied in all walks of life.Under the big data environment,massive big data provides convenience for regional tax risk control and strategic decision-making but also increases the difficulty of data supervision and management.By analyzing the status quo of big data and tax risk management,this paper finds many problems and puts forward effective countermeasures for tax risk supervision and strategic management by using big data.

关键词： big data Tax risk Strategic management

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Sports Prediction Model through Cloud Computing and big data Based on Artificial Intelligence Method

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Journal of Intelligent Learning Systems and Applications 2024年第2期16卷 53-79页

作者： Aws I. Abu Eid Achraf Ben Miled Ahlem Fatnassi Majid A. Nawaz Ashraf F. A. Mahmoud Faroug A. Abdalla Chams Jabnoun Aida Dhibi Firas M. Allan Mohammed Ahmed Elhossiny Salem Belhaj Imen Ben Mohamed Faculty of Computing Studies Arab Open University Amman Jordan Computer Science Department Science College Northern Border University Arar Kingdom of Saudi Arabia Artificial Intelligence and data Engineering Laboratory LR21ES23 Faculty of Sciences of Bizerte University of Carthage Tunis Tunisia Applied College Northern Border University Arar Kingdom of Saudi Arabia Faculty of Specific Education Mansoura University Mansoura Egypt

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

关键词： Artificial Intelligence Machine Learning Spark Apache big data SAIM

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Digital Transformation of Enterprise Human Resource Management Enabled by big data

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Proceedings of Business and Economic Studies 2024年第2期7卷 60-65页

作者： Zhefan Zhuang Union College Fujian Normal UniversityFuzhou 350000China

With the continuous development of big data technology,the digital transformation of enterprise human resource management has become a development trend.Human resources is one of the most important resources of enterprises,which is crucial to the competitiveness of enterprises.Enterprises need to attract,retain,and motivate excellent employees,thereby enhancing the innovation ability of enterprises and improving competitiveness and market share in the market.To maintain advantages in the fierce market competition,enterprises need to adopt more scientific and effective human resource management methods to enhance organizational efficiency and competitiveness.At the same time,this paper analyzes the dilemma faced by enterprise human resource management,points out the new characteristics of enterprise human resource management enabled by big data,and puts forward feasible suggestions for enterprise digital transformation.

关键词： big data Digital transformation Enterprise management Human resource management

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Research on the Development Strategy of Smart Tourism in Hainan Under the Background of big data

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Journal of Contemporary Educational Research 2024年第4期8卷 108-113页

作者： Limei Fu Hainan Vocational University of Science and Technology Haikou 571126Hainan ProvinceChina

Hainan is a major tourist province.It is urgent to promote the transformation and upgrading of Hainan’s tourism industry from a traditional service industry to a modern service industry by means of informatization.Smart tourism is a brand-new tourism form and operation mode of tourism transformation and upgrading.Integrating big data technology will make smart tourism more accurate in three aspects:tourism management,tourism service,and tourism marketing,and further enhance the satisfaction of the tourism experience.This paper studies the development status of smart tourism in Hainan,deeply summarizes its existing problems and causes,and puts forward the development strategy of smart tourism in Hainan to promote the healthy development of the tourism industry in Hainan.

关键词： big data Smart tourism Development strategy

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