Big Data Management Research Articles

Big data management, digital maintenance services and data analytics on intelligent operation: Basis for data management efficiency framework

Big data management, digital maintenance services and data analytics on intelligent operation: Basis for data management efficiency framework

Research and Implementation of a Classification Method of Industrial Big Data for Security Management

ABSTRACTPurpose/SignificanceWith the extensive adoption of cloud computing, big data, artificial intelligence, the Internet of Things, and other novel information technologies in the industrial field, the data flow in industrial companies is rapidly increasing, leading to an explosion in the total volume of data. Ensuring effective data security has become a critical concern for both national and industrial entities.Method/ProcessTo tackle the challenges of classification management of industrial big data, this study proposed an Information Security Triad Assessment‐Support Vector Machine (AIC‐ASVM) model according to information security principles. Building on national policy requirements, FIPS 199 standards, and the ABC grading method, a comprehensive classification framework for industrial data, termed “two‐layer classification, three‐dimensional grading,” was developed. By integrating the concept of Data Protection Impact Assessment (DPIA) from the GDPR, the classification of large industrial data sets was accomplished using a Support Vector Machine (SVM) algorithm.Result/ConclusionSimulations conducted using MATLAB yielded a classification accuracy of 96.67%. Furthermore, comparisons with decision tree and random forest models demonstrated that AIC‐ASVM outperforms these alternatives, significantly improving the efficiency of big data classification and the quality of security management.

Results of bracing adolescent idiopathic scoliosis in the context of clinical practice and the Scoliosis Research Society’s criteria: 5-year observational study from a German orthopaedic university hospital

BackgroundBrace therapy’s influence on adolescent idiopathic scoliosis’s (AIS) natural course is inconclusive.MethodsBrace-treated AIS patients from 2016 to 2020 were examined regarding four endpoints at brace weaning: surgery need, curve progress ≥ 6° and > 45°, and curve improvement ≥ 6°. Prediction variables’ influence was computed for the all-patients group and three subgroups (Subgroup 1: fulfilling the Scoliosis Research Society’s [SRS] criteria, Subgroup 2: initial Cobb angle < 25°, Subgroup 3: initial Cobb angle > 40°). According to the data characteristics, parametric and non-parametric tests and binary logistic regression were performed.ResultsThe research included 69 patients. Overall, after brace weaning surgery was recommended for 20.3% of them, curve progression was ≥ 6° in 23.2%, the Cobb angle was beyond 45° in 11.6%, and the Cobb angle improved by ≥ 6° in 20.3%. Patients needing surgery had a significantly higher initial Cobb angle (38.8° ± 9.8° vs 27.8° ± 7.6°, p < 0.001), lower Risser stages (p = 0.010), and higher Nash and Moe degrees (p = 0.030). Patients with curve progress ≥ 6° were younger at first curve notification (12.4 ± 1.5 vs 13.7 ± 1.7 years, p = 0.011) and older at menarche (13.4 ± 1.1 vs 12.6 ± 1.2 years, p = 0.037). Furthermore, 21.6% of all Risser 3 and 4 patients had still curve progress ≥ 6°. In-brace correction was significantly higher in patients with curve improvement ≥ 6° vs < 6° (54.0% ± 31.2% vs 31.9% ± 30.7%; p = 0.019). Nine patients fulfilled the SRS criteria, 22 had initial Cobb angles < 25°, and 11 had > 40°. Real brace wear (RBW) in all groups had no significant effect on outcome. Two significant subgroup differences were found: Surgery recommendation and curve progression beyond 45° were significantly more frequent in the initial Cobb angle > 40° subgroup.ConclusionsBrace effectiveness can be assumed because curve improvement was achieved in nearly one-fifth with sufficient in-brace correction. However, no significant influence of RBW on the outcome endpoints was demonstrated. To clarify conflicting results, big data management, including untreated patients, must be employed to further research AIS’s multifactorial influenced aetiology and course. Meanwhile, it is worth starting bracing in AIS in practice also beyond the SRS’s criteria.

Blockchain Factors in the Design of Smart-Media for E-Healthcare Management.

According to the current situation of deep aging globally, how to provide low-cost and high-quality medical services has become a problem that the whole society needs to consider. To address these challenges, we propose an e-healthcare management system leveraging the integration of the Internet of Things (IoT) and blockchain technologies. Our system aims to provide comprehensive, reliable, and secure one-stop services for patients. Specifically, we introduce a blockchain-based searchable encryption scheme for decentralized storage and real-time updates of electronic health records (EHRs). This approach ensures secure and efficient data traceability across medical equipment, drug supply chains, patient health monitoring, and medical big data management. By improving information processing capabilities, our system aspires to advance the digital transformation of e-healthcare services.

Big Data as An Emerging Paradigm in Organisations' Management: A Bibliometric Analysis

The Big Data Age defines the present era, and the globalisation of business makes it pressing to derive valuable insights from data so that organisations can make sustained decisions. There is no consensus in the literature on how organisations should guide the vast volume of data in value creation or galvanise performance gains. The study aims to address these gaps by reviewing the literature searching WoS using R. Bibliometrix. 4,019 documents were identified between 2008 and early February 2022 through a current mapping of Big Data in management. The results indicate a strong collaboration network among authors and a notable trend in Big Data, Big Data Analytics, Machine Learning, and Artificial Intelligence. These keywords reveal a concern for the predictive analysis of data and the emergence of new research trends, namely management, performance, decision-making, business and value creation, supporting the thesis that Big Data is an emerging paradigm in organisational management.

The Role of Digital Health Under Taiwan’s National Health Insurance System: Progress and Challenges

ABSTRACT Digital health covers a wide spectrum of applications of digital technologies in the healthcare field. As a new set of tools to support the health system in achieving its goals—improving access to care, quality of care, and system efficiency—digital health has significantly transformed the landscape of modern medicine and health care. This paper examines the role of digital health under Taiwan National Health Insurance, considering the profound impacts of digital health during the COVID-19 pandemic. It focuses specifically on big data management and analytics (MediCloud and My Health Bank/NHI Mobile Easy Access) and innovative service provision models (telemedicine). We discuss two imminent challenges that any health system is likely to encounter: digital trust and digital divide. For the digital divide, we assessed the use of telemedicine and its determinants during the COVID-19 pandemic. Our study shows that high-income levels and the presence of chronic or severe illness were positively correlated with the use of telemedicine. This observation suggests that poor people who have poorer health status were most likely to suffer from unmet needs for telemedicine. Enhancing cybersecurity to safeguard confidentiality, and effective communications with the public are fundamental and essential steps to regaining public trust in the digital era. When calling for more investment in digital technology, policy makers should be mindful of the potential digital divide across the demographic and socioeconomic strata, and specific policies should be devised to provide support to target the socially disadvantaged group.

The Influence of Social Media on College Choice among Undergraduates Majoring in Big Data Management and Application in China: Taking Guilin University of Electronic Technology as an Example

This study aims to investigate the influence of social media on college choice among undergraduates majoring in Big Data Management and Application in China. The study attempts to reveal how information on social media platforms such as Weibo, WeChat, and Zhihu influences the cognition and choice process of prospective students. By employing an online quantitative survey questionnaire, data were collected from the 2022 and 2023 classes of new students majoring in Big Data Management and Application at Guilin University of Electronic Technology. The aim was to evaluate the role of social media in their college choice process and understand the features and information that most attract prospective students. Social media has become a key factor influencing the college choice decision-making of undergraduates majoring in Big Data Management and Application in China. Students tend to obtain school information through social media platforms and use this information as an important reference in their decision-making process. Higher education institutions should strengthen their social media information dissemination, providing accurate, timely, and attractive information. It is also necessary to ensure effective management of social media platforms, maintain a positive reputation for the school on social media, and increase the interest and trust of prospective students. Simultaneously, educational decision-makers should consider incorporating social media analysis into their recruitment strategies to better attract new student enrollment. This study provides a new perspective for understanding higher education choice behavior in the digital age, particularly by revealing the importance of social media in the educational decision-making process. This has important practical and theoretical implications for higher education institutions, policymakers, and social media platform operators.

Digital Transformation in Talent Acquisition: An Investigation into the Integration of Big Data Analytics and Management Information Systems for Optimal Hiring Strategies

The primary goal was to empirically investigate the relationships amongst Big Data Analytics (BDA), Management Information System (MIS), optimal hiring strategies, leadership support, and talent acquisition process. This research aimed to fill crucial gaps in the literature by examining how technology adoption and leadership support influence the formulation and effectiveness of optimal hiring strategies, finally impacting talent acquisition results in the Chinese business landscape. Quantitative research was employed, targeting experts and decision-makers in human resource departments across numerous industries in China. The research used a random sampling approach to acquire facts through an online questionnaire, examining responses from 168 participants. Regression and mediation analyses, facilitated by SPSS, were applied to discover the relationships and mechanisms among BDA, MIS, optimal hiring strategies, leadership support, and talent acquisition success. The results discovered a significant and positive relationship between BDA and MIS with the optimal hiring techniques. Moreover, optimal hiring techniques have proven an advantageous association with talent acquisition success. The research similarly showed the mediating role of optimal hiring strategies in translating the advantages of BDA and MIS into talent acquisition success. Leadership support emerged as a moderator, amplifying the relationship between optimum hiring techniques and talent acquisition success. This study contributes empirical proof to the underexplored intersection of leadership support, technology, and talent hiring strategies. The findings offer practical insights for companies aiming to navigate the complexities of recruitment, emphasizing the significance of strategic decision-making, technology integration, and management endorsement in achieving talent acquisition success.

Big Data Analysis and Information Sharing for Innovation and Entrepreneurship Education

This study delves into the transformative potential of integrating big data analysis and information sharing in innovation and entrepreneurship education. Employing a comprehensive methodology encompassing K-Means Clustering, Decision Trees, Apriori Algorithm, and Neural Networks, the research investigates student engagement patterns, influential factors, collaborative relationships, and predictive modelling within educational settings. The findings reveal significant outcomes, with K-Means achieving a clustering precision of 75%, Decision Trees demonstrating an accuracy of 82%, Apriori Algorithm uncovering frequent itemsets with 68% support, and Neural Networks achieving a notable accuracy of 90%. Drawing insights from a diverse range of literature, including studies on big data management, demand prediction models, ecological approaches to entrepreneurship education, qualitative inquiries into startup strategies, applications of ICTs in education, and the impact of virtual gaming on SMEs’ growth, the research provides a robust foundation for understanding innovation and entrepreneurship education. This study contributes to both theoretical understanding and practical implications, guiding educators and policymakers in tailoring interventions and strategies to foster an adaptive and effective educational environment.

뉴욕경찰청의 알고리즘 예측치안 전략의 논쟁점

The purpose of this study is to examine the controversies surrounding the algorithmic predictive policing strategy adopted by the New York Police Department. NYPD is very active in pursuing algorithmic predictive policing strategies, but it is facing a variety of criticisms, including: First, personal information collection activities for predictive security violate the ideology of the Fourth Amendment to the U.S. Constitution, which prohibits invasion of privacy. Second, as the police rely on instructions generated by algorithmic predictions, police operations are becoming dehumanized. Third, the police exercise their police authority intensively against dangerous people or dangerous areas, and there is a risk that these people will eventually be negatively stigmatized and become criminals. Fourth, the police unilaterally exercise police power based on algorithmic predictions, rather than a two-way police service that reflects the needs of citizens or the community. Fifth, the efficiency of algorithmic predictive policing has not been verified, and the problem of contamination of used big data is being raised. Sixth, it is impossible to confirm whether the data provided by the police to private companies is opaque and secure. Seventh, there is the irrationality of Police power being replaced by private companies' for-profit businesses. Eighth, the New York Police Department is subject to monitoring of algorithmic predictive policing through the Local Law in Relation to Automated Decision Systems Used by Agencies and Public Oversight of Surveillance Technology(POST) Act, but the problems remain unresolved. Ninth, the New York Police Department continues to engage in legal disputes with human rights watchdog groups such as the Brennen Center over the non-disclosure of algorithmic predictive security information, losing most cases, raising questions about the legality of big data collection and management.

Data Lakehouse: A survey and experimental study

Efficient big data management is a dire necessity to manage the exponential growth in data generated by digital information systems to produce usable knowledge. Structured databases, data lakes, and warehouses have each provided a solution with varying degrees of success. However, a new and superior solution, the data Lakehouse, has emerged to extract actionable insights from unstructured data ingested from distributed sources. By combining the strengths of data warehouses and data lakes, the data Lakehouse can process and merge data quickly while ingesting and storing high-speed unstructured data with post-storage transformation and analytics capabilities. The Lakehouse architecture offers the necessary features for optimal functionality and has gained significant attention in the big data management research community. In this paper, we compare data lake, warehouse, and lakehouse systems, highlight their strengths and shortcomings, identify the desired features to handle the evolving challenges in big data management and analysis and propose an advanced data Lakehouse architecture. We also demonstrate the performance of three state-of-the-art data management systems namely HDFS data lake, Hive data warehouse, and Delta lakehouse in managing data for analytical query responses through an experimental study.

Bridging the Gap to 6G: Leveraging the Synergy of Standardization and Adaptability

The field of wireless network and communication technology is evolving from generation to generation from 1G to 6G as of now till expected to be deployed and used by 2030. It is to succeed in 5G and bring significant improvements in terms of connectivity, speed, and size in next-generation communication technology. 6G aims to deal with the rising need for more rapid information speed, low latency, and wider network coverage. This intelligent communication is proposed to meet these demands and enable new services and applications. This review paper highlights the key enablers and challenges involved in implementing intelligent communication beyond 5G. The paper identifies the research gaps for incorporating beyond 5G communication networks and outlines the possible 6G key objectives from a flexibility standpoint. It reviews infrastructure deployment, network densification, spectrum capacity and network energy efficiency in predecessors to 6G. This paper emphasizes the need for standardization and adaptation of research areas to revolutionize 6G wireless communication, focusing on areas like, ultra massive MIMO, Terahertz Communications, Cell-Free Communications, Intelligent Reflecting Surface, Visible Light Communication, Internet of Things, Big Data management, Artificial Intelligence, and network connectivity techniques.

Blockchain-based privacy preservation framework for preventing cyberattacks in smart healthcare big data management systems

Blockchain-based privacy preservation framework for preventing cyberattacks in smart healthcare big data management systems

Innovation and Patenting Activities During COVID-19 and Advancement of Biochemical and Molecular Diagnosis in the Post- COVID-19 Era.

The COVID-19 pandemic is to escalate globally and acquire new mutations quickly, so accurate diagnostic technologies play a vital role in controlling and understanding the epidemiology of the disease. A plethora of technologies acquires diagnosis of individuals and informs clinical management of COVID. Some important biochemical parameters for COVID diagnosis are the elevation of liver enzymes, creatinine, and nonspecific inflammatory markers such as C-reactive protein (CRP) and Interleukin 6 (IL-6). The main progression predictors are lymphopenia, elevated D-dimer, and hyperferritinemia, although it is also necessary to consider LDH, CPK, and troponin in the marker panel of diagnosis. Owing to the greater sensitivity and accuracy, molecular technologies such as conventional polymerase chain reaction (PCR), reverse transcription (RT)-PCR, nested PCR, loop-mediated isothermal amplification (LAMP), and xMAP technology have been extensively used for COVID diagnosis for some time now. To make so many diagnostics accessible to general people, many techniques may be exploited, including point of care (POC), also called bedside testing, which is developing as a portable promising tool in pathogen identification. Some other lateral flow assay (LFA)-centered techniques like SHERLOCK, CRISPR-Cas12a (AIOD-CRISPR), and FNCAS9 editor limited uniform detection assay (FELUDA), etc. have shown auspicious results in the rapid detection of pathogens. More recently, low-cost sequencing and advancements in big data management have resulted in a slow but steady rise of next-generation sequencing (NGS)-based approaches for diagnosis that have potential relevance for clinical purposes and may pave the way toward a better future. Due to the COVID-19 pandemic, various institutions provided free, specialized websites and tools to promote research and access to critically needed advanced solutions by alleviating research and analysis of data within a substantial body of scientific and patent literature regarding biochemical and molecular diagnosis published since January 2020. This circumstance is unquestionably unique and difficult for anyone using patent information to find pertinent disclosures at a specific date in a trustworthy manner.

Implementation of Agent Systems in Big Data Management: Integrating Artificial Intelligence for Data Mining Optimization

Background: The rapid growth of data generated across various domains necessitates advanced methodologies for effective data management and extraction of meaningful insights. Traditional data processing techniques often struggle with the volume, variety, and velocity of big data. The integration of Agent Systems and Artificial Intelligence (AI) presents a promising approach to address these challenges by enhancing the efficiency and effectiveness of data mining processes. Objective: This study aims to explore the implementation of Agent Systems in big data management, focusing on how the integration of AI can optimize data mining operations. By leveraging the capabilities of intelligent agents, we seek to improve the accuracy, speed, and scalability of data analysis. Methods: A hybrid research methodology was employed, combining a systematic literature review with an empirical case study. The literature review analyzed previous research on Agent Systems, AI, and big data management to identify key trends and challenges. The empirical case study involved deploying an AI-integrated Agent System within a large-scale data environment to evaluate its performance. Key performance indicators (KPIs) such as processing time, accuracy, and scalability were measured and analyzed. Results: The findings indicate that the integration of AI within Agent Systems significantly enhances the data mining process. The system demonstrated a reduction in processing time by 40%, an increase in data analysis accuracy by 25%, and improved scalability, handling larger datasets more efficiently compared to traditional methods. These improvements were attributed to the autonomous and adaptive nature of agent systems, which enabled dynamic data processing and real-time decision-making. Conclusion: The study concludes that the implementation of AI-integrated Agent Systems in big data management offers substantial benefits, including optimized data mining performance. This integration facilitates more efficient and effective data analysis, which is crucial for organizations dealing with large volumes of data. Future research should focus on further refining these systems and exploring their application across different sectors.

Factors influencing the use of big data within healthcare services: a systematic review.

Background: The emergence of big data holds the promise of aiding healthcare providers by identifying patterns and converting vast quantities of data into actionable insights facilitating the provision of precision medicine and decision-making. Objective: This study aimed to investigate the factors influencing use of big data within healthcare services to facilitate their use. Method: A systematic review was conducted in February 2024, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Database searches for articles published between 01 January 2020 and 18 February 2024 and included PubMed, Scopus, ProQuest and Cochrane Library. The Authority, Accuracy, Coverage, Objectivity, Date, Significance ( AACODS) checklist was used to evaluate the quality of the included articles. Subsequently, a thematic analysis was conducted on the findings of the review, using the Boyatzis approach. Results: A final selection of 46 studies were included in this systematic review. A significant proportion of these studies demonstrated acceptable quality, and the level of bias was deemed satisfactory. Thematic analysis identified seven major themes that influenced the use of big data in healthcare services. These themes were grouped into four primary categories: performance expectancy, effort expectancy, social influence, and facilitating conditions. Factors associated with "effort expectancy" were the most highly cited in the included studies (67%), while those related to "social influence" received the fewest citations (15%). Conclusion: This study underscored the critical role of "effort expectancy" factors, particularly those under the theme of "data complexity and management," in the process of using big data in healthcare services. Implications: Results of this study provide groundwork for future research to explore facilitators and barriers to using big data in health care, particularly in relation to data complexity and the efficient and effective management of big data, with significant implications for healthcare administrators and policymakers.

The Influence of Big Data Management Towards Big Data Decision-Making Capability in The Malaysian Public Sector

The study investigates the impact of Big Data Management (BDM) on Big Data Decision-Making Capability (BDDMC) in the Malaysian public sector. Utilizing Structural Equation Modelling (SEM) technique to analyze survey responses from 192 public sector employees, the research identifies that leadership focus on big data and using technology for big data significantly enhance BDDMC. Conversely, it finds that talent management for big data and the organizational culture surrounding big data do not have a notable impact in this context. These findings provide critical insights for public sector organizations, suggesting that prioritizing leadership and technology can improve the strategic use of big data in decision-making processes, particularly in the Malaysian public sector.

Innovative research aspects of big data modelling, management and analytics

Innovative research aspects of big data modelling, management and analytics

Big Data Analytics and Management Forecasting Behavior

SYNOPSIS This paper investigates whether the use of Big Data analytics by firms has a spillover effect on management forecasting behavior. Insights provided by Big Data could potentially improve firms’ ability to forecast earnings (supply channel) and investor demand for earnings information is likely higher for firms engaging in data analytics (demand channel). Using a text-based measure of firms’ commitments to and usage of Big Data analytics, we find that Big Data analytics usage is positively associated with the propensity to issue management earnings forecasts. Consistent with the “supply channel” explanation, we find that Big Data analytics usage is positively associated with management forecast accuracy as well. Also, supporting the “demand channel” explanation, we find that Big Data analytics usage is associated with greater analyst following. Our findings of improved disclosure following commitments to Big Data analytics highlight a potentially unintended benefit of the Big Data revolution.

Модель процесса формирования каталога сценариев для анализа и оценки функциональной безопасности автоматизированных систем вождения автомобилей

In the development of highly automated and unmanned vehicles, the auto industry is faced with a new set of problems associated with the need to standardize elements of automated driving systems and determine the rules for their testing and certification. Due to the acceleration of scientific and technological progress and the development of new technologies, previously used standards began to become obsolete as the practice of their application accumulated, and sometimes even before the approval of a particular technical regulation. In these conditions, a promising technical and legal solution may be the use of fuzzy logic in tools for aggregation and management of expert knowledge in the processes of certification and testing of highly automated vehicles and elements of their automated control systems. Fuzzy decision-making models use typical fuzzy situations that form catalogs of scenarios for testing vehicles and their systems. In this way, an expert system knowledge base can be formed in which knowledge engineers apply a set of test scenario or experiment parameters for testing and simulation. Determining the parameters of a new scenario, their similarity with previously formalized scenarios and the decision to include a particular scenario in the scenario catalog remains with the experts. The approaches used at the present stage to the formalization of expert knowledge are not acceptable for creating knowledge bases controlled by big data management systems or artificial neural networks, which are in the near future in the development of expert systems. The article proposes a method by which a knowledge engineer administering an expert system can automate the creation of a catalog of “fuzzy” test scenarios and simulation modeling of systems, by automatically searching for the maximum value of the states of these systems belonging to maps and catalogs of scenarios, with a probability specified by the expert, with using machine learning methods.