Big Data最新文献

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.

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, health care IoT-enabled body area networks (HIoT-BAN) 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 HIoT-BAN effectively predicts the rise in sports injury rehabilitation detection with faster digital health care based on IoT. The research concludes that the HIoT-BAN effectively indicates sports injury rehabilitation detection for digital health care. The experimental analysis of HIoT-BAN outperforms the IoT method in terms of performance, accuracy, prediction ratio, and mean square error rate.

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.

Mass transportation networks of cities or regions are interesting and important to be studied to get a picture of the properties of a somehow better topology and system of transportation. One way to do this lies on the basis of spatial information of stations and routes. As we show however interesting findings can be gained also if one studies the abstract network topologies of these systems. To get these abstract types of networks, we have developed a tool that can extract a network of connected stops from General Transit Feed Specification feeds. As we found during the development, service providers do not follow the specification in coherent ways, so as a kind of postprocessing we have introduced virtual stations to the abstract networks that gather close stops together. We analyze the effect of these new stations on the abstract map as well.

Cloud resource scheduling is one of the most significant tasks in the field of big data, which is a combinatorial optimization problem in essence. Scheduling strategies based on meta-heuristic algorithms (MAs) are often chosen to deal with this topic. However, MAs are prone to falling into local optima leading to decreasing quality of the allocation scheme. Algorithms with good global search ability are needed to map available cloud resources to the requirements of the task. Honey Badger Algorithm (HBA) is a newly proposed algorithm with strong search ability. In order to further improve scheduling performance, an Improved Honey Badger Algorithm (IHBA), which combines two local search strategies and a new fitness function, is proposed in this article. IHBA is compared with 6 MAs in four scale load tasks. The comparative simulation results obtained reveal that the proposed algorithm performs better than other algorithms involved in the article. IHBA enhances the diversity of algorithm populations, expands the individual's random search range, and prevents the algorithm from falling into local optima while effectively achieving resource load balancing.

Recommender system (RS) plays an important role in Big Data research. Its main idea is to handle huge amounts of data to accurately recommend items to users. The recommendation method is the core research content of the whole RS. However, the existing recommendation methods still have the following two shortcomings: (1) Most recommendation methods use only one kind of information about the user's interaction with items (such as Browse or Purchase), which makes it difficult to model complete user preference. (2) Most mainstream recommendation methods only consider the final consistency of recommendation (e.g., user preferences) but ignore the process consistency (e.g., user behavior), which leads to the biased final result. In this article, we propose a recommendation method based on the Entity Interaction Knowledge Graph (EIKG), which draws on the idea of collaborative filtering and innovatively uses the similarity of user behaviors to recommend items. The method first extracts fact triples containing interaction relations from relevant data sets to generate the EIKG; then embeds the entities and relations in the EIKG; finally, uses link prediction techniques to recommend items for users. The proposed method is compared with other recommendation methods on two publicly available data sets, Scholat and Lizhi, and the experimental result shows that it exceeds the state of the art in most metrics, verifying the effectiveness of the proposed method.

Pneumonia, caused by microorganisms, is a severely contagious disease that damages one or both the lungs of the patients. Early detection and treatment are typically favored to recover infected patients since untreated pneumonia can lead to major complications in the elderly (>65 years) and children (<5 years). The objectives of this work are to develop several models to evaluate big X-ray images (XRIs) of the chest, to determine whether the images show/do not show signs of pneumonia, and to compare the models based on their accuracy, precision, recall, loss, and receiver operating characteristic area under the ROC curve scores. Enhanced convolutional neural network (CNN), VGG-19, ResNet-50, and ResNet-50 with fine-tuning are some of the deep learning (DL) algorithms employed in this study. By training the transfer learning model and enhanced CNN model using a big data set, these techniques are used to identify pneumonia. The data set for the study was obtained from Kaggle. It should be noted that the data set has been expanded to include further records. This data set included 5863 chest XRIs, which were categorized into 3 different folders (i.e., train, val, test). These data are produced every day from personnel records and Internet of Medical Things devices. According to the experimental findings, the ResNet-50 model showed the lowest accuracy, that is, 82.8%, while the enhanced CNN model showed the highest accuracy of 92.4%. Owing to its high accuracy, enhanced CNN was regarded as the best model in this study. The techniques developed in this study outperformed the popular ensemble techniques, and the models showed better results than those generated by cutting-edge methods. Our study implication is that a DL models can detect the progression of pneumonia, which improves the general diagnostic accuracy and gives patients new hope for speedy treatment. Since enhanced CNN and ResNet-50 showed the highest accuracy compared with other algorithms, it was concluded that these techniques could be effectively used to identify pneumonia after performing fine-tuning.

"Industry 4.0" aims to build a highly versatile, individualized digital production model for goods and services. The carbon emission (CE) issue needs to be addressed by changing from centralized control to decentralized and enhanced control. Based on a solid CE monitoring, reporting, and verification system, it is necessary to study future power system CE dynamics simulation technology. In this article, a data-driven approach is proposed to analyzing the trajectory of urban electricity CEs based on empirical mode decomposition, which suggests combining macro-energy thinking and big data thinking by removing the barriers among power systems and related technological, economic, and environmental domains. Based on multisource heterogeneous mass data acquisition, effective secondary data can be extracted through the integration of statistical analysis, causal analysis, and behavior analysis, which can help construct a simulation environment supporting the dynamic interaction among mathematical models, multi-agents, and human participants.

The demand for intensive care units (ICUs) is steadily increasing, yet there is a relative shortage of medical staff to meet this need. Intensive care work is inherently heavy and stressful, highlighting the importance of optimizing these units' working conditions and processes. Such optimization is crucial for enhancing work efficiency and elevating the level of diagnosis and treatment provided in ICUs. The intelligent ICU concept represents a novel ward management model that has emerged through advancements in modern science and technology. This includes communication technology, the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data analytics. By leveraging these technologies, the intelligent ICU aims to significantly reduce potential risks associated with human error and improve patient monitoring and treatment outcomes. Deep learning (DL) and IoT technologies have huge potential to revolutionize the surveillance of patients in the ICUs due to the critical and complex nature of their conditions. This article provides an overview of the most recent research and applications of linical data for critically ill patients, with a focus on the execution of AI. In the ICU, seamless and continuous monitoring is critical, as even little delays in patient care decision-making can result in irreparable repercussions or death. This article looks at how modern technologies like DL and the IoT can improve patient monitoring, clinical results, and ICU processes. Furthermore, it investigates the function of wearable and advanced health sensors coupled with IoT networking systems, which enable the secure connection and analysis of various forms of patient data for predictive and remote analysis by medical professionals. By assessing existing patient monitoring systems, outlining the roles of DL and IoT, and analyzing the benefits and limitations of their integration, this study hopes to shed light on the future of ICU patient care and identify opportunities for further research.