Big Data最新文献

{"title":"Deep Learning-Based Decision Support System for Nurse Staff in Hospitals.","authors":"Jieyu Chen, Feilong He, Lihua Tang, Lingli Gu","doi":"10.1089/big.2024.0122","DOIUrl":"https://doi.org/10.1089/big.2024.0122","url":null,"abstract":"<p><p>To promote the informatization management of hospital human resources and advance the application of hospital information technology. The application of deep learning (DL) technologies in health care, particularly in hospital settings, has shown significant promise in enhancing decision-making processes for nurse staff. Utilizing a hospital management decision support system based on data warehouse theory and business intelligence technology to achieve multidimensional analysis and display of data. This research explores the development and implementation of a DL-Based Clinical Decision Support System (DL-CDSS) tailored for nurses in hospitals. DL-CDSS utilizes advanced neural network architectures to analyze complex clinical data, including patient records, vital signs, and diagnostic reports, aiming to assist nurses in making informed decisions regarding patient care. By leveraging large-scale datasets from Hospital Information Systems, DL-CDSS provides real-time recommendations for treatment plans, medication administration, and patient monitoring. The system's effectiveness is demonstrated through improved accuracy in clinical decision-making, reduction in medication errors, and optimized workflow efficiency. The system analyzes and displays nurses data from hospitals in terms of quantity, distribution, structure, forecasting, analysis reports, and peer comparisons, providing head nurses with multilevel, multiperspective data mining analysis results. Challenges such as data integration, model interpretability, and user interface design are addressed to ensure seamless integration into nursing practice, also concludes with insights into the potential benefits of DL-CDSS in promoting patient safety, enhancing health care quality, and supporting nursing professionals in delivering optimal care.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of the COVID-19 Pandemic on Stock Market Performance in G20 Countries: Evidence from Long Short-Term Memory with a Recurrent Neural Network Approach.","authors":"Pingkan Mayosi Fitriana, Jumadil Saputra, Zairihan Abdul Halim","doi":"10.1089/big.2023.0015","DOIUrl":"10.1089/big.2023.0015","url":null,"abstract":"<p><p>In light of developing and industrialized nations, the G20 economies account for a whopping two-thirds of the world's population and are the largest economies globally. Public emergencies have occasionally arisen due to the rapid spread of COVID-19 globally, impacting many people's lives, especially in G20 countries. Thus, this study is written to investigate the impact of the COVID-19 pandemic on stock market performance in G20 countries. This study uses daily stock market data of G20 countries from January 1, 2019 to June 30, 2020. The stock market data were divided into G7 countries and non-G7 countries. The data were analyzed using Long Short-Term Memory with a Recurrent Neural Network (LSTM-RNN) approach. The result indicated a gap between the actual stock market index and a forecasted time series that would have happened without COVID-19. Owing to movement restrictions, this study found that stock markets in six countries, including Argentina, China, South Africa, Turkey, Saudi Arabia, and the United States, are affected negatively. Besides that, movement restrictions in the G7 countries, excluding the United States, and the non-G20 countries, excluding Argentina, China, South Africa, Turkey, and Saudi, significantly impact the stock market performance. Generally, LSTM prediction estimates relative terms, except for stock market performance in the United Kingdom, the Republic of Korea, South Africa, and Spain. The stock market performance in the United Kingdom and Spain countries has significantly reduced during and after the occurrence of COVID-19. It indicates that the COVID-19 pandemic considerably influenced the stock markets of 14 G20 countries, whereas less severely impacting 6 remaining countries. In conclusion, our empirical evidence showed that the pandemic had restricted effects on the stock market performance in G20 countries.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"219-242"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138832891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investment Recommender System Model Based on the Potential Investors' Key Decision Factors.","authors":"Asefeh Asemi, Adeleh Asemi, Andrea Ko","doi":"10.1089/big.2022.0302","DOIUrl":"10.1089/big.2022.0302","url":null,"abstract":"<p><p>In this research, we propose an automatic recommender system for providing investment-type suggestions offered to investors. This system is based on a new intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS) that works with four potential investors' key decision factors (KDFs), which are system value, environmental awareness factors, the expectation of high return, and expectation of low return. The proposed system provides a new model for investment recommender systems (IRSs), which is based on the data of KDFs, and the data related to the type of investment. The solution of fuzzy neural inference and choosing the type of investment is used to provide advice and support the investor's decision. This system also works with incomplete data. It is also possible to apply expert opinions based on feedback provided by investors who use the system. The proposed system is a reliable system for providing suggestions for the type of investment. It can predict the investors' investment decisions based on their KDFs in the selection of different investment types. This system uses the K-means technique in JMP for preprocessing the data and ANFIS for evaluating the data. We also compare the proposed system with other existing IRSs and evaluate the system's accuracy and effectiveness using the root mean squared error method. Overall, the proposed system is an effective and reliable IRS that can be used by potential investors to make better investment decisions.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"197-218"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9432264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of Machine Learning-Based Extreme Value Theory in Stock Investment Risk Prediction: A Systematic Literature Review.","authors":"Melina Melina, Sukono, Herlina Napitupulu, Norizan Mohamed","doi":"10.1089/big.2023.0004","DOIUrl":"10.1089/big.2023.0004","url":null,"abstract":"<p><p>The stock market is heavily influenced by global sentiment, which is full of uncertainty and is characterized by extreme values and linear and nonlinear variables. High-frequency data generally refer to data that are collected at a very fast rate based on days, hours, minutes, and even seconds. Stock prices fluctuate rapidly and even at extremes along with changes in the variables that affect stock fluctuations. Research on investment risk estimation in the stock market that can identify extreme values is nonlinear, reliable in multivariate cases, and uses high-frequency data that are very important. The extreme value theory (EVT) approach can detect extreme values. This method is reliable in univariate cases and very complicated in multivariate cases. The purpose of this research was to collect, characterize, and analyze the investment risk estimation literature to identify research gaps. The literature used was selected by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and sourced from Sciencedirect.com and Scopus databases. A total of 1107 articles were produced from the search at the identification stage, reduced to 236 in the eligibility stage, and 90 articles in the included studies set. The bibliometric networks were visualized using the VOSviewer software, and the main keyword used as the search criteria is \"VaR.\" The visualization showed that EVT, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and historical simulation are models often used to estimate the investment risk; the application of the machine learning (ML)-based investment risk estimation model is low. There has been no research using a combination of EVT and ML to estimate the investment risk. The results showed that the hybrid model produced better Value-at-Risk (VaR) accuracy under uncertainty and nonlinear conditions. Generally, models only use daily return data as model input. Based on research gaps, a hybrid model framework for estimating risk measures is proposed using a combination of EVT and ML, using multivariable and high-frequency data to identify extreme values in the distribution of data. The goal is to produce an accurate and flexible estimated risk value against extreme changes and shocks in the stock market. Mathematics Subject Classification: 60G25; 62M20; 6245; 62P05; 91G70.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"161-180"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139486846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A MapReduce-Based Approach for Fast Connected Components Detection from Large-Scale Networks.","authors":"Sajid Yousuf Bhat, Muhammad Abulaish","doi":"10.1089/big.2022.0264","DOIUrl":"10.1089/big.2022.0264","url":null,"abstract":"<p><p>Owing to increasing size of the real-world networks, their processing using classical techniques has become infeasible. The amount of storage and central processing unit time required for processing large networks is far beyond the capabilities of a high-end computing machine. Moreover, real-world network data are generally distributed in nature because they are collected and stored on distributed platforms. This has popularized the use of the MapReduce, a distributed data processing framework, for analyzing real-world network data. Existing MapReduce-based methods for connected components detection mainly struggle to minimize the number of MapReduce rounds and the amount of data generated and forwarded to the subsequent rounds. This article presents an efficient MapReduce-based approach for finding connected components, which does not forward the complete set of connected components to the subsequent rounds; instead, it writes them to the Hadoop Distributed File System as soon as they are found to reduce the amount of data forwarded to the subsequent rounds. It also presents an application of the proposed method in contact tracing. The proposed method is evaluated on several network data sets and compared with two state-of-the-art methods. The empirical results reveal that the proposed method performs significantly better and is scalable to find connected components in large-scale networks.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"243-268"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139571864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Co-Movement and Asymmetric Relationships of Oil Prices on the Shipping Stock Returns: Evidence from Three Shipping-Flagged Companies from Germany, South Korea, and Taiwan.","authors":"Jumadil Saputra, Kasypi Mokhtar, Anuar Abu Bakar, Siti Marsila Mhd Ruslan","doi":"10.1089/big.2023.0026","DOIUrl":"10.1089/big.2023.0026","url":null,"abstract":"<p><p>In the last 2 years, there has been a significant upswing in oil prices, leading to a decline in economic activity and demand. This trend holds substantial implications for the global economy, particularly within the emerging business landscape. Among the influential risk factors impacting the returns of shipping stocks, none looms larger than the volatility in oil prices. Yet, only a limited number of studies have explored the complex relationship between oil price shocks and the dynamics of the liner shipping industry, with specific focus on uncertainty linkages and potential diversification strategies. This study aims to investigate the co-movements and asymmetric associations between oil prices (specifically, West Texas Intermediate and Brent) and the stock returns of three prominent shipping companies from Germany, South Korea, and Taiwan. The results unequivocally highlight the indispensable role of oil prices in shaping both short-term and long-term shipping stock returns. In addition, the research underscores the statistical significance of exchange rates and interest rates in influencing these returns, with their effects varying across different time horizons. Notably, shipping stock prices exhibit heightened sensitivity to positive movements in oil prices, while exchange rates and interest rates exert contrasting impacts, one being positive and the other negative. These findings collectively illuminate the profound influence of market sentiment regarding crucial economic indicators within the global shipping sector.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"181-196"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139736755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big Data Confidentiality: An Approach Toward Corporate Compliance Using a Rule-Based System.","authors":"Georgios Vranopoulos, Nathan Clarke, Shirley Atkinson","doi":"10.1089/big.2022.0201","DOIUrl":"10.1089/big.2022.0201","url":null,"abstract":"<p><p>Organizations have been investing in analytics relying on internal and external data to gain a competitive advantage. However, the legal and regulatory acts imposed nationally and internationally have become a challenge, especially for highly regulated sectors such as health or finance/banking. Data handlers such as Facebook and Amazon have already sustained considerable fines or are under investigation due to violations of data governance. The era of big data has further intensified the challenges of minimizing the risk of data loss by introducing the dimensions of Volume, Velocity, and Variety into confidentiality. Although Volume and Velocity have been extensively researched, Variety, \"the ugly duckling\" of big data, is often neglected and difficult to solve, thus increasing the risk of data exposure and data loss. In mitigating the risk of data exposure and data loss in this article, a framework is proposed to utilize algorithmic classification and workflow capabilities to provide a consistent approach toward data evaluations across the organizations. A rule-based system, implementing the corporate data classification policy, will minimize the risk of exposure by facilitating users to identify the approved guidelines and enforce them quickly. The framework includes an exception handling process with appropriate approval for extenuating circumstances. The system was implemented in a proof of concept working prototype to showcase the capabilities and provide a hands-on experience. The information system was evaluated and accredited by a diverse audience of academics and senior business executives in the fields of security and data management. The audience had an average experience of ∼25 years and amasses a total experience of almost three centuries (294 years). The results confirmed that the 3Vs are of concern and that Variety, with a majority of 90% of the commentators, is the most troubling. In addition to that, with an approximate average of 60%, it was confirmed that appropriate policies, procedure, and prerequisites for classification are in place while implementation tools are lagging.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"90-110"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71415222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Big Data Analytics on Decision-Making Within the Government Sector.","authors":"Laila Faridoon, Wei Liu, Crawford Spence","doi":"10.1089/big.2023.0019","DOIUrl":"10.1089/big.2023.0019","url":null,"abstract":"<p><p>The government sector has started adopting big data analytics capability (BDAC) to enhance its service delivery. This study examines the relationship between BDAC and decision-making capability (DMC) in the government sector. It investigates the mediation role of the cognitive style of decision makers and organizational culture in the relationship between BDAC and DMC utilizing the resource-based view of the firm theory. It further investigates the impact of BDAC on organizational performance (OP). This study attempts to extend existing research through significant findings and recommendations to enhance decision-making processes for a successful utilization of BDAC in the government sector. A survey method was adopted to collect data from government organizations in the United Arab Emirates, and partial least-squares structural equation modeling was deployed to analyze the collected data. The results empirically validate the proposed theoretical framework and confirm that BDAC positively impacts DMC via cognitive style and organizational culture, and in turn further positively impacting OP overall.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"73-89"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139405170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Sports Injury Rehabilitation Detection Based on IoT Models for Digital Health Care.","authors":"Zhiyong Wu, Zhida Huang, Nianhua Tang, Kai Wang, Chuanjie Bian, Dandan Li, Vumika Kuraki, Felix Schmid","doi":"10.1089/big.2023.0134","DOIUrl":"10.1089/big.2023.0134","url":null,"abstract":"<p><p>Physical therapists specializing in sports rehabilitation detection help injured athletes recover from their wounds and avoid further harm. Sports rehabilitators treat not just commonplace sports injuries but also work-related musculoskeletal injuries, discomfort, and disorders. Sensor-equipped Internet of Things (IoT) monitors the real-time location of medical equipment such as scooters, cardioverters, nebulizer treatments, oxygenation pumps, or other monitor gear. Analysis of medicine deployment across sites is possible in real time. Health care delivery based on digital technology to improve access, affordability, and sustainability of medical treatment is known as digital health care. The challenging characteristics of such sports injury rehabilitation for digital health care are playing position, game strategies, and cybersecurity. Hence, in this research, <i>health care IoT-enabled body area networks (HIoT-BAN)</i> have been designed to improve sports injury rehabilitation detection for digital health care. The health care sector may benefit significantly from IoT adoption since it allows for enhanced patient safety; health care investment management includes controlling the hospital's pharmaceutical stock and monitoring the heat and humidity levels. Digital health describes a group of programmers made to aid health care delivery, whether by assisting with clinical decision-making or streamlining back-end operations in health care institutions. A <i>HIoT-BAN</i> effectively predicts the rise in sports injury rehabilitation detection with faster digital health care based on IoT. The research concludes that the <i>HIoT-BAN</i> effectively indicates sports injury rehabilitation detection for digital health care. The experimental analysis of <i>HIoT-BAN</i> outperforms the IoT method in terms of performance, accuracy, prediction ratio, and mean square error rate.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"144-160"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consumer Segmentation Based on Location and Timing Dimensions Using Big Data from Business-to-Customer Retailing Marketplaces.","authors":"Fatemeh Ehsani, Monireh Hosseini","doi":"10.1089/big.2022.0307","DOIUrl":"10.1089/big.2022.0307","url":null,"abstract":"<p><p>Consumer segmentation is an electronic marketing practice that involves dividing consumers into groups with similar features to discover their preferences. In the business-to-customer (B2C) retailing industry, marketers explore big data to segment consumers based on various dimensions. However, among these dimensions, the motives of location and time of shopping have received relatively less attention. In this study, we use the recency, frequency, monetary, and tenure (RFMT) method to segment consumers into 10 groups based on their time and geographical features. To explore location, we investigate market distribution, revenue distribution, and consumer distribution. Geographical coordinates and peculiarities are estimated based on consumer density. Regarding time exploration, we evaluate the accuracy of product delivery and the timing of promotions. To pinpoint the target consumers, we display the main hotspots on the distribution heatmap. Furthermore, we identify the optimal time for purchase and the most densely populated locations of beneficial consumers. In addition, we evaluate product distribution to determine the most popular product categories. Based on the RFMT segmentation and product popularity, we have developed a product recommender system to assist marketers in attracting and engaging potential consumers. Through a case study using data from massive B2C retailing, we conclude that the proposed segmentation provides superior insights into consumer behavior and improves product recommendation performance.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"111-126"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71415223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}