Value Of Big Data Research Articles

Platform Ecosystem: The Logic of Digital Business Model Innovation

This paper examines the impact of big data capability and value network reconstruction on digital business platforms ecosystem cocreation value. Furthermore, it presents a mechanism model, with business model innovation as the mediating variable. The findings of this study are of considerable importance for digital business platforms seeking to enhance their platform ecosystem co-creation value, particularly in light of their big data capability and value network reconstruction.

The credibility assessment of Twitter/X users based organization objectives by heterogeneous resources in big data life cycle

Social network data, such as Twitter/X, is of Big Social Data type. Big social data describes people's social behaviors and interactions. They have high business value for decision-making in organizations. However, because of the anonymous nature of social network users, their credibility is ambiguous. Credibility expresses the accuracy and value of big social data. Despite extensive research on the credibility of big social data, most methods have not paid sufficient attention to the important dimensions of their assessment, including user expertise based on topic, selecting social network features, and labeling them. Furthermore, these methods cannot manage the time, high volume, and speed of big social data. To address these issues, this paper presents a novel model for assessing the credibility of Twitter/X users by integrating Twitter/X with Google Scholar. The model automatically defines users' credibility labels using Google Scholar. Machine learning feature selection methods also select features that affect the credibility of Twitter/X users based on the topic. This study uses Google Scholar and the BerTopic algorithm for effective topic modeling on Twitter/X. The model considers unrelated data management, dynamic user credibility, and organizing activities based on the Big Data lifecycle. Finally, using Linear Regression, Support Vector Regression, K-Nearest Neighbor, Random Forest, Classification and Regression Trees algorithms, the model predicts the credibility of Twitter/X users and proves that it performed better than similar models through Classification and Regression Trees. In addition, the model is generalizable for all organizational purposes due to the integration of heterogeneous resources and feature selection methods.

Research on Big Data Capability and Medical Service Performance

With the rapid development of information technology, Big data technology has been widely applied in various fields.In the field of medical services, Big data capabilities not only help improve the efficiency and quality of medical services, but also promote the innovative development of the medical industry.This article deeply analyzes the connotation of Big data capabilities and their application in improving medical service performance, and combines the practical work of large-scale general hospitals and community medical service institutions to analyze the specific value and application effect of Big data in medical service practice. At the same time, based on medical reform, it summarizes the challenges, coping strategies, and development trends of Big data in medical service performance practice, with the ultimate goal of providing reference and guidance for practical work.

Data sovereignty requirements for patient-oriented AI-driven clinical research in Germany

Abstract Background The rapidly growing quantity of health data presents researchers with ample opportunity for innovation. At the same time, exploitation of the value of Big Data poses various ethical challenges that must be addressed in order to fulfil the requirements of responsible research and innovation (Gerke et al. 2020; Howe III and Elenberg 2020). Data sovereignty and its principles of self-determination and informed consent are central goals in this endeavor. However, their consistent implementation has enormous consequences for the collection and processing of data in practice, especially given the complexity and growth of data in healthcare, which implies that artificial intelligence (AI) will increasingly be applied in the field due to its potential to unlock relevant, but previously hidden, information from the growing number of data (Jiang et al. 2017). Consequently, there is a need for ethically sound guidelines to help determine how data sovereignty and informed consent can be implemented in clinical research. Methods Using the method of a narrative literature review combined with a design thinking approach, this paper aims to contribute to the literature by answering the following research question: What are the practical requirements for the thorough implementation of data sovereignty and informed consent in healthcare? Results We show that privacy-preserving technologies, human-centered usability and interaction design, explainable and trustworthy AI, user acceptance and trust, patient involvement, and effective legislation are key requirements for data sovereignty and self-determination in clinical research. We outline the implications for the development of IT solutions in the German healthcare system.

Big Data Value Proposition in UK Facilities Management: A Structural Equation Modelling Approach

Big data analytics (BDA) has been introduced in the past few years in most industries as a factor capable of revolutionizing their operations by offering significant efficiency opportunities and benefits. To compete in this digital age, businesses must adopt a client-centric service model, founded on data delivering continuous value and achieving optimal performance, whilst also upgrading their own decision-making and reporting processes. This article aims to explore how UK FM organizations are currently capitalizing on BDA to drive innovation and ‘added value’ in their operations. The objective is to shed light on the initial BDA adoption efforts within the UK’s FM sector, particularly capturing the benefits experienced by FM organizations in relation to customer value and improved decision-making processes. Drawing upon exploratory sequential research including a qualitative stage with 12 semi-structured interviews and an industry-wide questionnaire survey with 52 responses, a novel fifteen-variable model for BDA outcomes was developed. Exploratory Factor Analysis (EFA) and a Higher-Order model using Partial Least Square Structural modelling (PLS-SEM) were used to validate the scale. The EFA output generated three dimensions with 14 items. The dimensions included Improved client value, FM business operations added value, and Improved efficiency added value. Furthermore, the results of PLS-SEM confirmed the validity of the scale items and the reflective–formative measurement model. The findings suggest that the contemporary digitization trend offers the FM service the unique opportunity to develop a smarter, client-centric strategy resulting in more personalized services and stronger customer relationships. Furthermore, efficient resource management and planning powered by analytics and data-driven insights emerge as a key driver for competitive differentiation in the field. As one of the first studies to develop and validate scale items measuring specific dimensions of BDA adoption outcomes, the study makes significant contributions to the literature.

Analysis of the Current Situation of Credit Investigation Management in China

This study provides a comprehensive analysis of credit investigation management in China, emphasizing its significance in building a social credit system and promoting national development. The literature review highlights the evolution of credit investigation in the era of big data, focusing on the integration of cloud computing, artificial intelligence, and blockchain technologies to enhance credit evaluation accuracy and efficiency. The research employs literature research, case analysis, and factor analysis methods to explore the current state and challenges of credit investigation management in Chinese universities. The study identifies the theoretical value of big data in credit investigation and underscores the importance of data governance. It also examines the origin and development of credit investigation management as a discipline, offering insights into curriculum design in universities. The findings reveal that while credit management education in China faces challenges, there are significant opportunities for growth and development. The study concludes with recommendations for strengthening research, standardizing courses, and investing in teaching resources to improve credit management education.

A quantitative causal analysis and optimization framework for inclusions of steel products

Inclusions generated during steelmaking and continuous casting processes could seriously reduce the mechanical properties of steel products and greatly increase the risk of corrosion failure. Much attention has been paid to perceiving the origin and classification of inclusions based on the process knowledge and mechanism model, while the value of industrial big data is underestimated. Machine learning (ML) methods have demonstrated potential in the processing of nonlinear and strong coupling relationships in industrial data. However, existing ML models are frequently unable to analyze causal relationships between manufacturing process, which is essential for the quality optimization. To address the limitation, this paper proposes a quantitative causal analysis and optimization framework. This framework starts with a feature selection module using propensity score matching (PSM) to measure the quantitative and reliable causal relationship between the process parameters and inclusions. Subsequently, a high-performance AutoML method, AutoGluon-Tabular (AGT), is applied to quantify the optimization range and verify the optimization effect. Specifically, AGT is used to estimate the inclusion level in each window of the process parameter to be optimized, choose the best window of which to determine adjustment strategies, and eventually verify the change of inclusion levels after optimization. The comparative experiments based on the real data collected from a steel company demonstrate that the proposed framework reduces the inclusion rate by 8.85%, which is effective, efficient and has guiding significance for the inclusions control of steel products.

Big Data Analytics and Culture

The existing big data analytics measures were developed without considering the cultural dimensions of developing countries. This research aims to develop and validate measures for big data Vs and cultural big data analytics and study their impacts on the developing countries' big data value proposition. Following MacKenzie's and Shiau and Huang's scale development procedures, data was collected twice from individuals in a developing country to refine the scale and reexamine its properties. PLS methods were used to study the impacts of big data Vs and cultural big data analytics on the value proposition. The findings revealed that big data analytics snobbism and conformism positively impact big data value proposition. Similarly, big data volume, velocity, and variety positively impact the value proposition. Paradoxically, big data veracity and variability do not significantly affect the value proposition. Surprisingly, big data analytics fatalism negatively impacts the value proposition. Theoretical and practical contributions were offered.

Enterprise asset management and accounting using the Internet of Things and big data

With the rapid development of information technology, the Internet of Things (IoT) and big data have gradually become the core tools of modern enterprise management and decision-making. This study explores the application and potential value of IoT and big data in the field of enterprise assets and accounting. Through strategic questionnaire design and multi-channel data collection, a corresponding data model is constructed, and the effectiveness of the model is further verified. The results show that these technologies can not only improve the management efficiency of enterprises, but also provide more accurate and timely data support for corporate decision-making. However, in practical applications, enterprises also need to face data integrity, technology updates, and a host of other challenges. This study not only provides enterprises with an opportunity to gain an in-depth understanding of these technologies, but also provides valuable implications for future research and applications.

Regulating the Rational Use of Antimicrobial Drugs and Improving the Quality of Healthcare Big Data: A Development and Usability Study as A Prelude to AI Assisted Healthcare

Background: Healthcare big data has become an important strategic resource, and the study of applying it to the rational application of antimicrobial drugs is of great significance in improving and optimizing the various tasks of medicine and health and promoting the development of social health. Based on its own characteristics, the issue of data quality has important research value in promoting data output and application. Objective: To optimize the status quo of antimicrobial drug use in medical institutions, to explore the value of healthcare big data, and to explore the data governance methods in the era of digital intelligence in order to build a good ecosystem for the application of healthcare big data. Methods: This paper constructs a set of intelligent full-closed-loop antimicrobial rational application management platform integrated with data quality, adopts PDCA cycle management mode, analyzes the problems in the process of applying healthcare big data and gives the corresponding solutions, authorizes according to the personnel's duties, and guarantees the data security. Results: Since the platform went online in January 2021, the antimicrobial drug use intensity indicator of a hospital has decreased year by year from 38 to 34.4 as of June 2023, effectively standardizing the use of antimicrobial drugs. The time consumed for the statistics of antimicrobial use intensity related data has decreased from an average of 30min to 2min, which has significantly improved the work efficiency.Healthcare professionals are able to monitor indicator data in real time, with or without abnormal cases, and fully grasp the trend of indicators. At the same time, the patient's medication can be viewed at any time, and the condition can be grasped in a timely manner. Conclusions: The platform not only effectively solves the problem of information barriers, but also accurately determines the data source and statistical logic of indicator collection, reflecting the actual clinical diagnosis and treatment, and at the same time greatly reduces labor costs and optimizes the workflow.The practical experience of platform construction has important reference significance for the governance and application of healthcare big data, which provides data support and technical guarantee for the scientific, standardized and refined management of hospitals.

Big data security and privacy in healthcare: A systematic review and future research directions

Big data security involves protecting healthcare data from unauthorised use and access and making any changes, while privacy entails ensuring the confidentiality of individual patient information within the datasets. The two are crucial in ensuring that big data is effectively applied for research and personalised care, compromising the sensitivity of healthcare data and medical information. Consequently, as the amount of data in the healthcare sector grows exponentially, securing sensitive data becomes increasingly crucial while effectively utilising big data analytics. The systematic review explores the issues and challenges associated with big data security and privacy in healthcare. Through reference to resource-based view theory, this paper seeks to examine the present state of research in this area, identify gaps in the existing literature, and propose strategies for future research. The search strategy looked at the data between 2013 and 2022 and yielded 93 studies from four databases: Scopus, PubMed, ScienceDirect and IEEE Xplore. From these studies, 18 papers are reviewed. The findings reveal that while significant progress has been made in securing healthcare data, numerous challenges still need to be considered. Overall, this review highlights the importance of continued research to ascertain that sensitive healthcare data is kept secure while allowing for the enhanced application of big data analytics. It contributes to the security and privacy problems associated with using big data in healthcare, measures for addressing these challenges, the healthcare value of big data, and foci for future research.

Concept and Application of Industrial Heritage Planning Decision in Big Data Era

With the rapid development of big data technology, more and more fields are realizing the importance of big data and applying it to various decision-making processes. Industrial heritage planning decisions are no exception. This article will explore the concept and application of industrial heritage planning and decision-making in the era of big data, aiming to emphasize the importance and application value of big data in industrial heritage planning and decision-making. This article analyzes the development relationship between the construction and transformation of urban industrial utilization and the theory and practice of industrial heritage. Through theoretical and practical technical analysis of specific regions, the value utilization of industrial simulation design has been improved. Through in-depth data mining, the government found that the industrial structure of the region is dominated by traditional manufacturing, lacking support from high-tech industries, and the pollution problem of the surrounding environment is also relatively serious. In response to these issues, corresponding planning plans have been formulated, including measures to introduce high-tech industries, optimize industrial structure, and improve environmental quality. By implementing these measures, the comprehensive strength of the region has been significantly improved and the economic transformation and upgrading have been successfully achieved.

Fair concordance between Google Trends and Danish ornithologists in the assessment of temporal trends in Danish bird populations highlights the informational value of big data

The ongoing depletion of natural systems and associated biodiversity decline is of growing international concern. Climate change is expected to exacerbate anthropogenic impacts on wild populations. The scale of impact on ecosystems and ecosystem services will be determined by the impact on a multitude of species and functional groups, which due to their biology and numbers are difficult to monitor. The IPCC has argued that surveillance or monitoring is critical and proposed that monitoring systems should be developed, which not only track developments but also function as “early warning systems.” Human populations are already generating large continuous datasets on multiple taxonomic groups through internet searches. These time series could in principle add substantially to current monitoring if they reflect true changes in the natural world. We here examined whether information on internet search frequencies delivered by the Danish population and captured by Google Trends (GT) appropriately informs on population trends in 106 common Danish bird species. We compared the internet search activity with independent equivalent population trend assessments from the Danish Ornithological Society (BirdLife Denmark/DOF). We find a fair concordance between the GT trends and the assessments by DOF. A substantial agreement can be obtained by omitting species without clear temporal trends. Our findings suggest that population trend proxies from internet search frequencies can be used to supplement existing wildlife population monitoring and to ask questions about an array of ecological phenomena, which potentially can be integrated into an early warning system for biodiversity under climate change.

Data sharing and big data in health professions education: Ottawa consensus statement and recommendations for scholarship

Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare.

Research on Curriculum Design of Data Mining in Different Majors

With the increasing attention paid to the application value of big data, data mining has become an important major course not only in big data, computer and information technology, but also in most majors in colleges and universities. This course involves the explanation of classic algorithms of data mining as well as the application of classical algorithms of data mining, so it usually has both theoretical and practical hours in the composition of teaching form. Because of the differences in the knowledge structure of students in different majors, there should be differences in the theoretical teaching and practical teaching design of data mining courses among different majors. The teaching content should be selected according to the characteristics and needs of students in different majors, and should also consider the development and trends of big data technology. In addition, in order to improve the teaching quality and effectiveness of data mining courses, it is also necessary to use modern teaching methods and technologies to support teaching, such as online teaching resources, flipped classroom teaching, etc. In short, it is necessary to carry out targeted teaching design for data mining courses in different majors to match the basic situation of students in different majors, ensure better teaching effect and play a better role in different fields of specialization.

Research and application of carbon emission data sharing model based on privacy computing

In the context of the dual-carbon goal, improving the level of scientific and technological innovation of modern carbon emissions and achieving comprehensive green development are not only one of the trends and important reform directions of today’s world development, but also the core content of achieving sustainable development. Taking the power industry as an important breakthrough, this paper uses blockchain technology and privacy computing technology to integrate data from the government, power grid, taxation and banks, and proposes a blockchain-based privacy computing algorithm for carbon emission scores to create an execution environment for trusted sharing of carbon emission data, expand channels for carbon emission data collection, and give full play to the value of big data on carbon emissions. Promote the realization of dual carbon goals as soon as possible.

Determinants of the Use of Big Data Technologies by Organizations in Russian Regions

A scientific discussion is unfolding around data as a new factor of production that contributes to the transformation of traditional sectors of the economy, industrial integration, and ensures interregional interaction. At the same time, the question of the relationship with such traditional production factors as labor and capital needs to be answered. The study aims to identify the determinants of organizations' use of Big Data at a regional level. The main hypothesis of the study suggests that the key determinants of organizations' use of big data technologies are digital labor, digital capital and the socio-economic characteristics of regions. In the study we proposed a modified knowledge production function which was tested on open data from the Federal State Statistics Service for 85 regions of Russia in 2021-2022. Panel models were constructed using the method of least squares, generalized feasible least squares. The study presents illustrative material made using cartograms and graphical methods. The results of the study distinguished the spatial heterogeneity in the use of Big Data technologies in Russia's regions and differentiation of the regions by volume of digital capital and digital labor. Panel data models with random effects confirmed the positive impact of digital labor and digital capital on organizations' use of Big Data. Among the socio-economic characteristics of regions as determinants of the use of big data technologies, significant effects were obtained for the share of urban population, gross regional product and share of innovation costs. The study identifies the determinants of the development of the data economy in Russian regions, considering geographic, technological, and economic differentiation. The theoretical significance of the study lies in the proposal of the author's concept of a modified knowledge production function, which can be used as a fundamental basis for the development of the theory of data economics. The practical significance of the study lies in the validity of the value of Big Data, the use of which can help institutions and government authorities find new opportunities for the development of the data economy, taking into account regional differentiation, improving the methodology for monitoring the use of digital technologies by organizations, and identifying the key factors influencing the use of Big Data technologies by organizations.

Network Mathematical Virtual Data Analysis Model Based on Deep Learning Algorithm

In today's era of big data, people need to extract valuable and nutritious information from these data, but for the huge database, manual browsing and manual retrieval by ordinary users are obviously inefficient. Network data analysis can help users to study network data and deeply understand the embedded value of big data, which is popular in both civil and industrial and academic circles. This article aimed to use intelligent mathematical models to assist in the construction of network data analysis models and enabled the model to be repeatedly trained in the training set based on deep learning algorithms, becoming increasingly able to identify patterns between data.

IProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis

Large-scale omics studies have generated a wealth of mass spectrometry–based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature–related, and correlation analyses, including protein–protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein–protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.

Construction of integrated platform for emergency clinical scientific research based on big data

To explore clinical rules based on the big data of the emergency department of the Second Affiliated Hospital of Guangzhou Medical University, and to establish an integrated platform for clinical research in emergency, which was finally applied to clinical practice. Based on the hospital information system (HIS), laboratory information system (LIS), emergency specialty system, picture archiving and communication systems (PACS) and electronic medical record system of the Second Affiliated Hospital of Guangzhou Medical University, the structural and unstructured information of patients in the emergency department from March 2019 to April 2022 was extracted. By means of extraction and fusion, normalization and desensitization quality control, the database was established. In addition, data were extracted from the database for adult patients with pre screening triage level III and below who underwent emergency visits from March 2019 to April 2022, such as demographic characteristics, vital signs during pre screening triage, diagnosis and treatment characteristics, diagnosis and grading, time indicators, and outcome indicators, independent risk factors for poor prognosis in patients were analyzed. (1) The data of 338 681 patients in the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to April 2022 were extracted, including 15 modules, such as demographic information, triage information, visit information, green pass and rescue information, diagnosis information, medical record information, laboratory examination overview, laboratory information, examination information, microbiological information, medication information, treatment information, hospitalization information, chest pain management and stroke management. The database ensured data visualization and operability. (2) Total 140 868 patients with pre-examination and triage level III and below were recruited from the emergency department database. The gender, age, type of admission to the hospital, pulse, blood pressure, Glasgow coma scale (GCS) and other indicators of the patients were included. Taking emergency admission to operating room, emergency admission to intervention room, emergency admission to intensive care unit (ICU) or emergency death as poor prognosis, the poor prognosis prediction model for patients with pre-examination and triage level III and below was constructed. The receiver operator characteristic curve and forest map results showed that the model had good predictive efficiency and could be used in clinical practice to reduce the risk of insufficient emergency pre-examination and triage. The establishment of high-quality clinical database based on big data in emergency department is conducive to mining the clinical value of big data, assisting clinical decision-making, and improving the quality of clinical diagnosis and treatment.