Big Data Resources Research Articles

Establishment and application of infectious disease monitoring, early warning and disposal system

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan's health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

Optimizing Hadoop Data Locality: Performance Enhancement Strategies in Heterogeneous Computing Environments

As organizations increasingly harness big data for analytics and decision-making, the efficient processing of massive datasets becomes paramount. Hadoop, a widely adopted distributed computing framework, excels in processing large-scale data. However, its performance is contingent on effective data locality, which becomes challenging in heterogeneous computing environments comprising diverse hardware resources. This research addresses the imperative of enhancing Hadoop’s data locality performance in heterogeneous computing environments. The study explores strategies to optimize data placement and task scheduling, considering the diverse characteristics of nodes within the infrastructure. Through a comprehensive analysis of Hadoop’s data locality algorithms and their impact on performance, this work proposes novel approaches to mitigate challenges associated with disparate hardware capabilities. Weighted Extreme Learning Machine Technique (Weighted ELM) with the Firefly Algorithm (WELM-FF) is used in the proposed work. The integration of Weighted Extreme Learning Machine (WELM) with the Firefly Algorithm holds promise for enhancing machine learning models in the context of large-scale data processing. The research employs a combination of theoretical analysis and practical experiments to evaluate the effectiveness of the proposed enhancements. Factors such as network latency, disk I/O, and CPU capabilities are taken into account to develop a holistic framework for improving data locality and, consequently, overall Hadoop performance. The findings presented in this study contribute valuable insights to the field of distributed computing, offering practical recommendations for organizations seeking to maximize the efficiency of their Hadoop deployments in heterogeneous computing environments. By addressing the intricacies of data locality, this research strives to enhance the scalability and performance of Hadoop clusters, thereby facilitating more effective utilization of big data resources.

The Impact of Big Data Capability on Business Model Transformation：Mediated by Knowledge Renewal and moderated by Resource Slack

The traditional enterprises have been significantly impacted by the digital economy and the COVID-19 pandemic, leading to a more dynamic and complex business environment. Business model innovation has become essential for enterprises to adapt effectively to this complexity. This study aims to examine the impact of big data capabilities on business model transformation. Enterprises from four regions in Jiangxi Province—Shangrao, Pingxiang, Nanchang, and Ganzhou—were selected as the research subjects, covering various industries such as technology and information technology, financial services, healthcare, manufacturing, retail, and energy. Data from 800 enterprises were collected using stratified random sampling and questionnaires, and analyzed empirically using structural equation modeling. The results demonstrate that big data capabilities significantly positively influence business model transformation, with knowledge renewal acting as a mediator between big data capabilities and business model transformation. These findings integrate the utilization of big data capabilities, knowledge renewal, and resource slack, facilitating business model transformation and enabling enterprises to achieve value growth through efficient resource sharing and management.

Evaluation of the Fire Impact of Cellulose-Based Indoor Building Finishing Materials According to Changes in Room Size Aspect Ratio

In modern society, the size of buildings tends to expand due to technological advances. However, while prioritizing performance design and specific building materials, fire research has fallen short of creating a comprehensive fire characteristic database for building materials. This study presents a plan for building a big data resource to evaluate the fire performance of cellulose-based flame retardant building finishing material in buildings of varying sizes. The three types of building finishing materials applied in this study are polyurethane foam, MDF, and cellulose-based building finishing materials. The variables were determined based on the floor area, and the change in floor area was calculated using the aspect ratio, a dimensionless number. Data analysis utilized the Fire Dynamics Simulator (FDS) to determine the time required to meet life safety standards for temperature, visibility, and Fractional Effective Dose (FED). The results confirm a correlation between the safe evacuation time (ASET) and increasing floor area. Additionally, the study demonstrates that cellulose-based flame-retardant building finishing material effectively maintains safe evacuation times even with increasing floor areas, as evidenced by increases of 41.0 s, 13.2 s, and 97.5 s in temperature, visibility, and FED, respectively.

Research on Effective Supply Strategies of Informationization Teaching Resources under the Background of "Double Reduction"

The quality of education after the "double reduction" needs to be guaranteed by deepening education and teaching reform. In order to better implement the "double reduction", it is necessary to apply information technology teaching methods to promote educational reform. At the same time, deepening information technology teaching reform is also an effective measure to promote the common and balanced development of compulsory education. In the society where information technology teaching is constantly developing, information resources are everywhere. In the context of "double reduction", the study of effective supply strategies for high-quality information technology education resources is conducive to the realization of reducing burden, increasing efficiency, and improving the quality of education environment. On the basis of explaining how information technology education resources can empower the implementation of "double reduction", this study proposes effective supply strategies for information technology education resources from the perspectives of precise promotion of teaching resources and teaching services, hierarchical supply of after-school service resources, big data resources to assist school work, and transformation of service models for off campus institutions.

Research on pediatric hereditary kidney disease: from now to the future

Hereditary kidney disease is an important cause of chronic kidney disease in children. With the progress of genome sequencing, single-cell technology, and organoid cultures, the research on hereditary kidney disease has entered a new era. How to integrate big data resources, discover new disease-causing genes, and develop effective treatment methods will be the focus of future research. This article discusses the classification, research progress, challenges and prospects of pediatric hereditary kidney disease, so as to provide valuable insights into the research of hereditary kidney disease in children.

Distributed Architectures for Big Data Analytics in Cloud Computing: A Review of Data-Intensive Computing Paradigm

Big Data challenges are prevalent in various fields, including economics, business, public administration, national security, and scientific research. While it offers opportunities for productivity and scientific breakthroughs, it also presents challenges in data capture, storage, analysis, and visualization. This paper provides a comprehensive overview of Big Data applications, opportunities, challenges, and current techniques and technologies to address these issues. This study presents a system for managing big data resources using cloud for the development of data-intensive applications. It addresses even the challenges related to technologies that combine cloud computing with other allied technologies and devices. In addition, the increasing volume, velocity, and variety of data in the era of Big Data necessitate advanced methods for data processing and management. This study delves into the intricacies of data scalability, real-time processing, and the integration of diverse data types. Furthermore, it explores the role of machine learning algorithms and artificial intelligence in extracting meaningful insights from massive datasets.

A big data quality evaluation method based on group heterogeneity rationality perception information fusion

Big data is complex, and it is difficult to evaluate big data quality with only a few evaluation subjects. This paper designs a universal evaluation method for big data quality based on the idea of group heterogeneity rationality perception information fusion. Group evaluation information is usually large-scale and heterogeneous. The linear programming technique for multidimensional analysis of preference (LINMAP) model has the performance of large-scale evaluation information processing. Because the existing LINMAP models cannot effectively process group evaluation information with heterogeneous rationality, the LINMAP model of heterogeneous rationality (HR-LINMAP) based on group evaluation information is proposed. The research results show: under certain conditions, the LINMAP model of bounded rationality (BR-LINMAP) can be transformed into the LINMAP model of perfect rationality (PR-LINMAP), and the HR-LINMAP is the general form of PR-LINMAP and BR-LINMAP. The scientific research big data resources with mature trading markets are selected as the evaluation objects, and HR-LINMAP is used to analyze the big data quality evaluation cases. The empirical analysis shows that the HR-LINMAP model is feasible and effective, which provides a new idea for big data quality evaluation.

Research on the Digital Transformation of the Whole Industry Chain of Guangdong Supply and Marketing Cooperative

Guangdong Supply and Marketing Cooperative serves as an important institution for the “Three Rural Areas” in the Guangdong-Hong Kong-Macao Greater Bay Area. Its digital transformation and optimization serve as powerful supports for rural revitalization. This paper systematically reviews the main practices and effectiveness of the digital transformation within the Guangdong Supply and Marketing Cooperative. It also analyzes the current development dilemmas faced in the construction of digital supply and marketing. Additionally, it proposes targeted solutions, including building a big data resource base, optimizing the digital supply and marketing cloud platform, developing digital public-type agricultural social service applications, establishing a new model of rural e-commerce, enhancing the traceability management system for agricultural products, and strengthening the construction of the digital human resources system. These proposals aim to further promote the strategy for revitalizing the countryside.

Exploring the informal learning of zero waste lifestyle in Malaysia with big data analytics

Malaysia's solid waste generation is showing a worrying increasing trend, while public awareness towards achieving zero waste remains low. As a big data resource, social media gives valuable insight into the public's perspective on zero waste and, as such, may be fully utilized as an active informal learning platform to lessen the reliance on formal environmental education. Using big data analytics on Instagram, this study aimed to assess the public knowledge, attitude and practice concerning zero waste lifestyle in Malaysia in order to develop an informal learning strategy on social media. Purposive data sampling was conducted on Phantombuster using zero waste-related Instagram hashtags, which yielded 1723 high engagement posts and 1500 comments from 35 identified hashtags. The recorded posts were analyzed using descriptive statistics and sentiment analysis on Python's TextBlob. A total of 94.3% of posts were published by public and private sector accounts, highlighting their vital role in facilitating active knowledge sharing across the online zero waste communities. The sentiment analysis results indicated 41.3% of comments were fairly positive, while 36.1% were more objective and knowledge oriented, acknowledging the collective individual actions that have initiated influential social change in Malaysia. This study advances the existing literature on zero waste and informal learning by recommending the use of big data analytics on social media in the local context. Only with full commitment from all parties to raising public awareness about waste management will the zero waste nation be realized.

Measuring Innovation and Collaboration System using Big Data: A Case Study about Ho Chi Minh City

Abstract Academic, government, and industry researchers have attempted to build some theoretical and empirical ways to understanding how to innovate and interact in data- driven contexts in recent years. However, it does not appear to go above and beyond our expectations in terms of offering fresh views and resources to investigate this trend in terms of measurement. In this brief study, I show how Dimensions.ai, a new academic big data resource, can be used to assess knowledge-based innovation processes in emerging and transitional countries like Vietnam. This article investigates creativity and collaboration in Ho Chi Minh City using a “n-tuple helix” framework based on Loet Leydesdorff’s sociological theory of communication. From a social network standpoint, I argued that a city’s innovation system is inextricably linked to its position in intercity and global co-authorship networks. This shows how the helix model and applications can be utilized to discover a data-driven society.

Web of venom: exploration of big data resources in animal toxin research.

Research on animal venoms and their components spans multiple disciplines, including biology, biochemistry, bioinformatics, pharmacology, medicine, and more. Manipulating and analyzing the diverse array of data required for venom research can be challenging, and relevant tools and resources are often dispersed across different online platforms, making them less accessible to nonexperts. In this article, we address the multifaceted needs of the scientific community involved in venom and toxin-related research by identifying and discussing web resources, databases, and tools commonly used in this field. We have compiled these resources into a comprehensive table available on the VenomZone website (https://venomzone.expasy.org/10897). Furthermore, we highlight the challenges currently faced by researchers in accessing and using these resources and emphasize the importance of community-driven interdisciplinary approaches. We conclude by underscoring the significance of enhancing standards, promoting interoperability, and encouraging data and method sharing within the venom research community.

The role of big data in the formation of supply chain platform for new forms of online retail

PurposeThe purpose of this study is to explore the relationship between big data and supply chain platform in China’s retail industry. With the emergence of big data resources and technologies, the business pattern of new retail advocates the combination of online and offline channels. Supply chain platform plays a key role in the implementation of retail activities, which has gradually become a research hotspot in the cross field of operations management and information system.Design/methodology/approachThrough the method of literature review and case study, this study empirically explores how big data shapes supply chain platform to support new forms of online retail by grounded theory.FindingsThe model framework is validated by reliability test and coding method to process survey materials. The results identify the overall antecedents of supply chain platform and reveal positive effects between big data and new retail. The findings help firm managers build a big data-driven supply chain to support new retail.Originality/valueThere are insufficient studies on theoretical frameworks and interaction relationships among big data, supply chain platform and new retail.

KPI-HGNN: Key provenance identification based on a heterogeneous graph neural network for big data access control

Dynamic access control of big data has received much attention in recent years because of the characteristics of dynamic generation and multi-source aggregation of big data resources. Provenance-based access control defines dependency paths between resource states through provenance data, and it can dynamically realize access control according to different stages and states of resources. However, big data systems involve huge resources, and the state transition process is complex. The existing research on provenance-based access control techniques faces problems such as low efficiency of manually formulating dependency paths, redundant information in provenance data, and uneven distribution of resources. To solve these problems, this paper proposes a key provenance identification framework KPI-HGNN based on the heterogeneous graph neural network. In the framework, a community detection algorithm based on the heterogeneous graph neural network is designed to realize automatic identification and division of corresponding regions of big data resources through feature fusion of multiple types of nodes and edges. Meanwhile, a key node identification algorithm based on the heterogeneous graph attention network is designed to identify the key node in each community by weighted aggregation of the neighboring node attention coefficients. Besides, a key dependency path discovery algorithm is designed, and access control rules are automatically generated based on key dependency paths. The experimental results indicate that the community detection clustering index of the proposed method is better, the Top-5% key node identification accuracy is higher, and the resource coverage rate and the average percentage number product of key nodes of the provenance-based access control rules are better than those of the baseline method. These results indicate that the proposed key provenance identification framework can efficiently solve the problems in identifying key provenance information faced by dynamic access control of big data.

Comparing the influence of big data resources on medical knowledge recall for staff with and without medical collaboration platform

BackgroundThis study aims to examine how big data resources affect the recall of prior medical knowledge by healthcare professionals, and how this differs in environments with and without remote consultation platforms.MethodThis study investigated two distinct categories of medical institutions, namely 132 medical institutions with platforms, and 176 medical institutions without the platforms. Big data resources are categorized into two levels—medical institutional level and public level—and three types, namely data, technology, and services. The data are analyzed using SmartPLS2.Results(1) In both scenarios, shared big data resources at the public level have a significant direct impact on the recall of prior medical knowledge. However, there is a significant difference in the direct impact of big data resources at the institutional level in both scenarios. (2) In institutions with platforms, for the three big data resources (the medical big data assets and big data deployment technical capacity at the medical institutional level, and policies of medical big data at the public level) without direct impacts, there exist three indirect pathways. (3) In institutions without platforms, for the two big data resources (the service capability and big data technical capacity at the medical institutional level) without direct impacts, there exist three indirect pathways.ConclusionsThe different interactions between big data, technology, and services, as well as between different levels of big data resources, affect the way clinical doctors recall relevant medical knowledge. These interaction patterns vary between institutions with and without platforms. This study provides a reference for governments and institutions to design big data environments for improving clinical capabilities.

State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation.

Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.

Emerging Trends in Big Data Analysis in Computational Biology and Bioinformatics in Health Informatics: A Case Study on Epilepsy and Seizures.

Advanced technology innovations allow cost-effective, high-throughput profiling of biological systems. It enabled genome sequencing in days using advanced technologies (e.g., next-generation sequencing, microarrays, and mass spectrometry). Since technology has been developed, massive biological data (e.g., genomics, proteomics) has been produced cheaply, allowing the "big data" era to create new opportunities to solve medical and biological complications in many disciplines-preventive medicine, biology, Personalized Medicine, gene sequencing, healthcare, and industry. Computational biology and bioinformatics are interdisciplinary fields that develop and apply computational methods (e.g., analytical methods, mathematical modeling, and simulation) to analyze large collections of biological data, such as genetic sequences, cell populations, or protein samples, to make new predictions or discover new biology. Biological data storage, mining, and analysis have challenges because data is much more heterogeneous. In this study, the big data resources of genomics, proteomics, and metabolomics have been explored to solve biological problems using big data analysis approaches. The goal is to build a network of relationship-based gene-disease associations to prioritize phenotypes common to epilepsy and seizure disease. Through network analysis, The 10 seed genes, 22 associated genes, 132 microRNAs, and 38 transcription factors have been identified that have a direct effect on all forms of epilepsy and seizures. The majority of seed genes, according to the results of a functional analysis of seed genes, are involved in the acetylcholine-gated channel complex (10%) and the heterotrimeric G-protein complex (10%) pathways related to cellular components, followed by a role in the regulation of action potential (20%) and positive regulation of vascular endothelial growth factor production (20%) in Epilepsy and Seizures pathways related to biological processes. This study might provide insight into the workings of the disease and shows the importance of continued research into epilepsy and other conditions that can trigger seizure activity.

Exploring the Necessity of Building a Big Data Resource Base for Chinese Dance Drama

Chinese Dance Drama has always been tightly linked with China's time and culture, and is an independent and distinctive part of the diverse landscape of contemporary Chinese dance. With the trend of market economy, technological development and data era, the development of Chinese dance drama is worthy of deep thinking by dancers, and the necessity of establishing a big data resource base for Chinese dance drama is not only the cry of the times, but also the demand and choice of the internal development of dance drama itself.

Unleashing the power of big data for platform firms: A configuration analysis

AbstractThis study investigates the impact of big data on platform performance using a multidimensional perspective and the fuzzy set qualitative comparative analysis (fsQCA) method. We identify two pathways for platforms to leverage big data to achieve performance improvement: “volume‐centric configuration” and “volume‐talent synergistic configuration.” Our results indicate that big data volume and talent of big data are the core conditions impacting platform performance, but an increase in these factors does not always equate to an improvement in performance. The study makes two substantial contributions: (1) identifying the configurations of big data that lead to high performance of the platform and illuminating the complex interplay of causality between the multidimensional nature of big data resources and their supporting conditions and (2) measuring the multidimensionality of big data resources in platform firms through the utilization of operational data of the app in question. This study has significant practical implications, providing guidance to platform firms on identifying appropriate big data resources to gain a competitive advantage and utilizing supporting conditions to address deficiencies in data assets.

Application and Reflection of Big Data in the Primary Mathematics Curriculum

With the vigorous development of science and technology in China, especially the wide application of big data, the concepts and practices of education and teaching are also being improved and optimized, and many new methods, strategies and means have emerged. In the primary school mathematics curriculum under the background of big data, teachers can make full and effective use of big data resources to provide students with more colorful teaching resources and bring students more lively classroom teaching. Based on the relevant understanding of big data and its development prospects, this paper introduces the application of big data in the primary school mathematics classroom, and combines reflection to put forward several suggestions for primary school mathematics teachers to use big data to improve teaching.