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Huang, Yue, Hu Liu, and Jing Pan. "Identification of data mining research frontier based on conference papers." International Journal of Crowd Science 5, no. 2 (2021): 143–53. http://dx.doi.org/10.1108/ijcs-01-2021-0001.
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Purpose Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers. Design/methodology/approach This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords. Findings Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich. Originality/value This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.
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Nnachi, Alum Benedict, Echegu Darlington Arinze, and Aleke Jude Uchechukwu. "Exploring the Frontiers of Data Analysis: A Comprehensive Review." INOSR APPLIED SCIENCES 12, no. 1 (2024): 62–68. http://dx.doi.org/10.59298/inosras/2024/12.1.62680.
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The process of assessing, cleansing, transforming, and interpreting data to find trends, patterns, or insights that might guide choices and help manage problems is known as data analysis. Data analysis is a leading light on the cutting edge of contemporary research, revealing the path of knowledge across many areas. It includes the methodical examination of data to find trends, patterns, and insights that are helpful for the analytical and creative processes. This review also examines how data analysis is developing, emphasizing new approaches, paradigms, viewpoints, and graphical data displays. In addition to the significant improvements brought about by artificial intelligence, deep learning, and machine learning, it emphasizes statistical inference, exploratory data analysis, and data pretreatment. One of the main ideas behind this review was to use a systematic literature review approach along with meta-analysis techniques to look for new developments and trends in how data is analyzed in a lot of different areas. The article also addresses how data visualization could improve comprehension and dissemination of the results. In promoting responsible data use and legal rules, it also looks at how analytics affects society, the law, and ethical issues. The evaluation underscores the diverse disciplines that employ data analysis, underscoring the need for interdisciplinary coherence and comprehensible algorithms. Finally, this thorough research offers recommendations for analyzing and determining the boundaries of data analysis, in addition to offering insightful viewpoints and opinions that are helpful for academics, professionals, and decision-makers. If we just stay up to speed with the latest advancements and strive to be more, we can utilize data analysis to its fullest potential for complicated problems and positively impact society. Keywords: Data mining, big data analytics, machine learning, algorithms, and data analysis and statistics
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Qi, Shaojie, Fengrui Hua, Shengyuan Xu, Zheng Zhou, and Feng Liu. "Trends of global health literacy research (1995–2020): Analysis of mapping knowledge domains based on citation data mining." PLOS ONE 16, no. 8 (2021): e0254988. http://dx.doi.org/10.1371/journal.pone.0254988.
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Background During uncertainties associated with the COVID-19 pandemic, effectively improving people’s health literacy is more important than ever. Drawing knowledge maps of health literacy research through data mining and visualized measurement technology helps systematically present the research status and development trends in global academic circles. Methods This paper uses CiteSpace to carry out a metric analysis of 9,492 health literacy papers included in Web of Science through mapping knowledge domains. First, based on the production theory of scientific knowledge and the data mining of citations, the main bodies (country, institution and author) that produce health literacy knowledge as well as their mutual cooperation (collaboration network) are both clarified. Additionally, based on the quantitative framework of cocitation analysis, this paper introduces the interdisciplinary features, development trends and hot topics of the field. Finally, by using burst detection technology in the literature, it further reveals the research frontiers of health literacy. Results The results of the BC measures of the global health literacy research collaboration network show that the United States, Australia and the United Kingdom are the major forces in the current international collaboration network on health literacy. There are still relatively very few transnational collaborations between Eastern and Western research institutions. Collaborations in public environmental occupational health, health care science services, nursing and health policy services have been active in the past five years. Research topics in health literacy research evolve over time, mental health has been the most active research field in recent years. Conclusions A systematic approach is needed to address the challenges of health literacy, and the network framework of cooperation on health literacy at regional, national and global levels should be strengthened to further promote the application of health literacy research. In the future, we anticipate that this research field will expand in two directions, namely, mental health literacy and eHealth literacy, both of which are closely linked to social development and issues. The results of this study provide references for future applied research in health literacy.
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An, Ran, Yuan Luo, Wen-Feng Chen, Muhammad Sohaib, and Mei-Zi Liu. "Global trends and knowledge-relationship of symptom clusters in cancer research: a bibliometric analysis over the past 20 years." Frontiers of Nursing 10, no. 3 (2023): 273–88. http://dx.doi.org/10.2478/fon-2023-0031.
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Abstract Objective To use CiteSpace and VOSviewer to investigate the scientific production in the field of symptom clusters in cancer research. Methods The search was performed using the terms “symptom clusters,” “cancer,” and “oncology” on the Web of Science Core Collection database. The retrieval time was from 2001 to 2021, which covers the last 2 decades. Based on the production theory of scientific knowledge and the data mining of citations, data pertaining to the annual publications, journals, countries, organizations, authors, and keywords that produce symptom clusters in cancer research, as well as their cooperation (collaboration network), were extracted, and then both were clarified by the software tools VOSviewer (version 1.6.16) and CiteSpace (version 6.1.R2). Results A total of 1796 publications were retrieved between 2001 and 2021, and 473 relevant publications were included after screening. The results showed an increasing trend in published articles. The United States had the largest number of publications (261/473, 55.18%), followed by China and Canada. The University of California, San Francisco, was the most productive institution. Current research hotspots included the analysis of symptom clusters and symptom management in patients with breast cancer and lung cancer, as well as any advanced cancer and cancer cachexia; fatigue-related symptom clusters and depression-anxiety symptom cluster; and the impacts of symptom clusters on quality of life. The research frontiers included analysis between health-related quality of life and symptom clusters, data mining in symptom clusters, research on the mental health status of cancer patients, and study of the mechanism and biological pathways of symptom clusters. Conclusions The study provides insight into the global research perspective for the scientific progress on cancer symptom clusters, which suggests a growing scientific interest in this field, and more studies are warranted to guide symptom management.
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ZENG, Wenjing, Kai ZHOU, and Yiqun XIONG. "Advances and Review of Machine Learning Applications in Urban Studies from 2005 to 2020." Chinese Geography Sciences Review 1, no. 1 (2023): 16–30. http://dx.doi.org/10.48014/cgsr.20220711001.
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Machine learning, as a new method for data mining and problem prediction, has been widely used in various fields of urban studies in recent years, which requires a periodical summary of relevant literature. Start with data types, selection and preprocessing, this paper introduces the characteristics and applicability of various machine learning algorithms, and analyzes the cross-fields, hot spots, frontiers and trends of machine learning and urban studies from 2005 to 2020 by using Citespace. Second, focusing on the application of supervised machine learning algorithms from relevant literature in the past five years, a review is made from four main aspects including urban traffic, urban ecology, physical geography, human geography, and the tentative explorations of unsupervised learning, semi-supervised learning and reinforcement learning method in urban studies are unscrambled as well. Finally, the advantages of machine learning methods are summarized, and its proposed that the application potential of various machine learning methods in multiple fields and perspectives of urban research should be explored in the future, and the cutting-edge trend of efficient combination of intelligent technology and methods with urban research should be grasped.
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Hou, Yajing, Lijun Xu, and Lu Chen. "Hotspots and Cutting-Edge Visual Analysis of Digital Museum in China Using Data Mining Technology." Computational Intelligence and Neuroscience 2022 (May 25, 2022): 1–16. http://dx.doi.org/10.1155/2022/7702098.
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During the last several years, the building and development of digital museums has grown in importance as a study issue of increasing importance. On the other hand, systematic and extensive literature study on digital museums is rare in the academic community throughout the world. This paper employs data mining technology to conduct a comprehensive analysis of the total amount of academic literature, research hotspots, frontiers, and trends in the field of digital museums in China since the beginning of the twenty-first century, including both historical and contemporary data. In this research, the CNIK database and the CiteSpace program are utilized. The findings revealed that the quantity of published literature expanded significantly between 2000 and 2021, with some variations along the way, but that the general growth rate remained consistent. Colleges and universities are the driving force behind academic research in the field of digital museums; research institutes and big museums play a key part in the academic research that is being conducted by digital museums. Cooperation between research institutes, on the other hand, is severely lacking. Furthermore, the advancement of digital technology is an unavoidable byproduct of the efforts to transform the digital museum into a smart museum, as previously said. When it comes to digital museum development in the postepidemic period, the optimization and updating of a user-centered information service platform is the most important step toward long-term success. In order to maintain the richness of Chinese traditional culture while also meeting the expanding cultural requirements of the general public, China’s digital museum research has as its ultimate objective the construction of sustainable digital museums that are appropriate for the country’s national conditions. The findings also demonstrate that the construction of a Chinese Digital Museum is a study issue with distinct Chinese features that has the potential to contribute to the preservation of Chinese cultural heritage, both tangible and intangible. This research gives insights into the following aspects: researchers and practitioners from across the world will work together to promote a better knowledge of the building and growth of the digital museum in China, among other things.
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Qin, Fangling, Ying Zhu, Tianqi Ao, and Ting Chen. "The Development Trend and Research Frontiers of Distributed Hydrological Models—Visual Bibliometric Analysis Based on Citespace." Water 13, no. 2 (2021): 174. http://dx.doi.org/10.3390/w13020174.
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Based on the bibliometric and data visualization analysis software Citespace, this study carried out document statistics and information mining on the Web of Science database and characterized the distributed hydrological model knowledge system from 1986 to 2019. The results show a few things: (1) from 1986 to 2019, the United States and China accounted for 41% of the total amount of publications, and they were the main force in the field of distributed hydrological model research; (2) field research involves multiple disciplines, mainly covering water resources, geology, earth sciences, environmental sciences, ecology and engineering; (3) the frontier of field research has shifted from using distributed hydrological models in order to simulate runoff and nonpoint source environmental responses to the coupling of technologies and products that can obtain high-precision, high-resolution data with distributed hydrological models. (4) Affected by climate warming, the melting of glaciers has accelerated, and the spatial distribution of permafrost and water resources have changed, which has caused a non-negligible impact on the hydrological process. Therefore, the development of distributed hydrological models suitable for alpine regions and the response of hydrological processes to climate change have also become important research directions at present.
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Zhang, Changlu, Qiong Yang, Jian Zhang, Liming Gou, and Haojie Fan. "Topic Mining and Future Trend Exploration in Digital Economy Research." Information 14, no. 8 (2023): 432. http://dx.doi.org/10.3390/info14080432.
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This work proposes a new literature topic clustering analysis framework, based on which the topics of digital-economy-related studies are condensed. First, we calculated the word vector of keywords using the FastText model, and then the keywords were merged according to semantic similarity. A hierarchical clustering method based on the Jaccard coefficient was employed to cluster the domain documents. Finally, the information gain method was applied to estimate the high-gain feature words for each category of topics. Based on the above framework, 23 categories of research topics were formed. We divided these topics into layers of digital technology, convergence innovation and digital governance, and we constructed a three-level digital economy research framework. Thereafter, the current hot spots and frontier trends were derived based on the number and growth rate of the literature. Our study revealed that the research on digital technology, which is the basic layer of the digital economy, has waned. The field related to the integration and innovation of digital technology and the real economy was the current research focus, among which the results with respect to “New Business Forms in the Digital Age”, “Circular Economy” and “Gig Economy” were abundant. The problems of the unbalanced development of the digital economy and digital monopoly have strengthened research on digital governance. Furthermore, research on “Regional Digital Economy”, “Chinese Digital Economy” and “Data Management” is in its initial stage and is a potential area of future research.
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Borgman, Christine L. "Whose text, whose mining, and to whose benefit?" Quantitative Science Studies 1, no. 3 (2020): 993–1000. http://dx.doi.org/10.1162/qss_a_00053.
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Scholarly content has become more difficult to find as information retrieval has devolved from bespoke systems that exploit disciplinary ontologies to keyword search on generic search engines. In parallel, more scholarly content is available through open access mechanisms. These trends have failed to converge in ways that would facilitate text data mining, both for information retrieval and as a research method for the quantitative social sciences. Scholarly content has become open to read without becoming open to mine, due both to constraints by publishers and to lack of attention in scholarly communication. The quantity of available text has grown faster than has the quality. Academic dossier systems are among the means to acquire more quality data for mining. Universities, publishers, and private enterprise may be able to mine these data for strategic purposes, however. On the positive front, changes in copyright may allow more data mining. Privacy, intellectual freedom, and access to knowledge are at stake. The next frontier of activism in open access scholarship is control over content for mining as a means to democratize knowledge.
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Zhang, Ting, Juan Chen, Yan Lu, Xiaoyi Yang, and Zhaolian Ouyang. "Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network." PLOS ONE 17, no. 8 (2022): e0273355. http://dx.doi.org/10.1371/journal.pone.0273355.
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Objectives This paper aimed to identify the technology frontiers of artificial intelligence-assisted pathology based on patent citation network. Methods Patents related to artificial intelligence-assisted pathology were searched and collected from the Derwent Innovation Index (DII), which were imported into Derwent Data Analyzer (DDA, Clarivate Derwent, New York, NY, USA) for authority control, and imported into the freely available computer program Ucinet 6 for drawing the patent citation network. The patent citation network according to the citation relationship could describe the technology development context in the field of artificial intelligence-assisted pathology. The patent citations were extracted from the collected patent data, selected highly cited patents to form a co-occurrence matrix, and built a patent citation network based on the co-occurrence matrix in each period. Text clustering is an unsupervised learning method, an important method in text mining, where similar documents are grouped into clusters. The similarity between documents are determined by calculating the distance between them, and the two documents with the closest distance are combined. The method of text clustering was used to identify the technology frontiers based on the patent citation network, which was according to co-word analysis of the title and abstract of the patents in this field. Results 1704 patents were obtained in the field of artificial intelligence-assisted pathology, which had been currently undergoing three stages, namely the budding period (1992–2000), the development period (2001–2015), and the rapid growth period (2016–2021). There were two technology frontiers in the budding period (1992–2000), namely systems and methods for image data processing in computerized tomography (CT), and immunohistochemistry (IHC), five technology frontiers in the development period (2001–2015), namely spectral analysis methods of biomacromolecules, pathological information system, diagnostic biomarkers, molecular pathology diagnosis, and pathological diagnosis antibody, and six technology frontiers in the rapid growth period (2016–2021), namely digital pathology (DP), deep learning (DL) algorithms—convolutional neural networks (CNN), disease prediction models, computational pathology, pathological image analysis method, and intelligent pathological system. Conclusions Artificial intelligence-assisted pathology was currently in a rapid development period, and computational pathology, DL and other technologies in this period all involved the study of algorithms. Future research hotspots in this field would focus on algorithm improvement and intelligent diagnosis in order to realize the precise diagnosis. The results of this study presented an overview of the characteristics of research status and development trends in the field of artificial intelligence-assisted pathology, which could help readers broaden innovative ideas and discover new technological opportunities, and also served as important indicators for government policymaking.
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Chen, Kai, Xiaoping Lin, Han Wang, et al. "Visualizing the Knowledge Base and Research Hotspot of Public Health Emergency Management: A Science Mapping Analysis-Based Study." Sustainability 14, no. 12 (2022): 7389. http://dx.doi.org/10.3390/su14127389.
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Public health emergency management has been one of the main challenges of social sustainable development since the beginning of the 21st century. Research on public health emergency management is becoming a common focus of scholars. In recent years, the literature associated with public health emergency management has grown rapidly, but few studies have used a bibliometric analysis and visualization approach to conduct deep mining and explore the characteristics of the public health emergency management research field. To better understand the present status and development of public health emergency management research, and to explore the knowledge base and research hotspots, the bibliometric method and science mapping technology were adopted to visually evaluate the knowledge structure and research trends in the field of public health emergency management studies. From 2000 to 2020, a total of 3723 papers related to public health emergency management research were collected from the Web of Science Core Collection as research data. The five main research directions formed are child prevention, mortality from public health events, public health emergency preparedness, public health emergency management, and coronavirus disease 2019 (COVID-19). The current research hotspots and frontiers are climate change, COVID-19 and related coronaviruses. Further research is needed to focus on the COVID-19 and related coronaviruses. This study intends to contribute inclusive support to related academia and industry in the aspects of public health emergency management and public safety research, as well as research hotspots and future research directions.
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Klongthong, Worasak, Veera Muangsin, Chupun Gowanit, and Nongnuj Muangsin. "Chitosan Biomedical Applications for the Treatment of Viral Disease: A Data Mining Model Using Bibliometric Predictive Intelligence." Journal of Chemistry 2020 (December 28, 2020): 1–12. http://dx.doi.org/10.1155/2020/6612034.
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Chitosan has attracted increasing attention from researchers in the pharmaceutical and biomedical fields as a potential agent for the prevention and treatment of infectious diseases. However, identifying the development of emerging technologies related to this biopolymer is difficult, especially for newcomers trying to understand the research streams. In this work, we designed and implemented a research process based on a bibliometric predictive intelligence model. Our aim is to glean detailed scientific and technological trends through an analysis of publications that include certain word phrases and related research areas. Cross correlation, factor mapping, and the calculation of “emergent” scores were also used. A total of 1,612 scientific papers on chitosan technology related to viral disease treatment published between 2010 and 2020 were retrieved from the Web of Science. Results from the keyword modelling quantitatively highlight three major frontier research and development topic groups: drug delivery and adjuvants, vaccines and immune response, and tissue engineering. More specifically, the emergent scores show that much of the chitosan-based treatment for viral diseases is in the in vitro stage of development. Most chitosan applications are in pharmacology/pharmacy and immunology. All results were confirmed by experts in the field, which indicates that the validated process can be applied to other fields of interest.
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Tian, Hua, and Jie Chen. "A bibliometric analysis on global eHealth." DIGITAL HEALTH 8 (January 2022): 205520762210913. http://dx.doi.org/10.1177/20552076221091352.
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Background The current coronavirus disease 2019 pandemic highlights the potential of eHealth. Drawing the knowledge map of eHealth research through data mining and visual analysis technology was helpful to systematically present the research status and future trends of global academic circles. Methods Based on the web of Science Core Collection (SCIE/SSCI) database, using bibliometric theory and visual analysis technology, this work analyzed the global eHealth research publications from 2000 to 2021, and introduced the interdisciplinary characteristics, hot topics and future trends in this field. Results A total of 10188 authors, 891 journals, 3586 institutions, 98 countries using 12 languages had conducted eHealth research in the world. The United States, the Netherlands, Australia and the United Kingdom were the main forces and international cooperation. However, the international co-operation between Eastern and Western countries was still relatively few. The frontier of global eHealth research mainly focused on #0eHealth innovation, #1physical activity, #2generalised anxiety disorder, #3lightweight authentication protocol, #4 eHealth information, #5technology readiness, #6 ehealth literacy scale, #7family carer, #8citance analysis, #9 guiding patient. Clusters #3 lightweight authentication protocol and #9 guiding patient were the latest clusters, indicating the research trend and direction of eHealth in the future. Conclusions Cooperation network framework at the regional, national and global levels and the cooperation of multidisciplinary teams with complementary backgrounds and expertise were needed to realize the in-depth popularization and application of eHealth knowledge. Interdisciplinary international cooperation should be the trend of eHealth research in the future.
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Xiao, Wei, Mingxia Liu, and Xubing Chen. "Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review." Applied Sciences 12, no. 18 (2022): 9290. http://dx.doi.org/10.3390/app12189290.
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The underground intelligent load-haul-dump vehicle (LHD) is a product of the deep integration of traditional LHD with information network technology, automatic controlling and artificial intelligence technology. It gathers the functions of environmental perception, autonomous driving and fault diagnosis in one machine and exhibits higher safety and greater efficiency than traditional LHD. Hence, it is a particularly important piece of underground mining equipment for building green, safe and smart mines. Taking the studies about intelligent LHD collected by CNKI and WOS databases from 1980 to 2022 as a sample data source, employing Citespace visual analysis software for key feature extraction from the documents, statistical analysis was conducted to clarify the current research progress and the frontier topics of the intelligent LHD academia in the past 40 years, in relation to the future development trends. The development history and application status of underground intelligent LHD was expounded in this article, summarizing the research status at home and abroad from four aspects: ore heap perception and modeling technology, trajectory planning method of bucket shoveling, autonomous navigation technology, real-time monitoring and intelligent fault diagnosis technology. The demerits and merits of the technologies were reviewed as well, with future developing and researching trends of the underground intelligent LHD concluded.
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Liu, Huailan, Rui Zhang, Yufei Liu, and Cunxiang He. "Unveiling Evolutionary Path of Nanogenerator Technology: A Novel Method Based on Sentence-BERT." Nanomaterials 12, no. 12 (2022): 2018. http://dx.doi.org/10.3390/nano12122018.
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In recent years, nanogenerator technology has developed rapidly with the rise of cloud computing, artificial intelligence, and other fields. Therefore, the quick identification of the evolutionary path of nanogenerator technology from a large amount of data attracts much attention. It is of great significance in grasping technical trends and analyzing technical areas of interest. However, there are some limitations in previous studies. On the one hand, previous research on technological evolution has generally utilized bibliometrics, patent analysis, and citations between patents and papers, ignoring the rich semantic information contained therein; on the other hand, its evolution analysis perspective is single, and it is difficult to obtain accurate results. Therefore, this paper proposes a new framework based on the methods of Sentence-BERT and phrase mining, using multi-source data, such as papers and patents, to unveil the evolutionary path of nanogenerator technology. Firstly, using text vectorization, clustering algorithms, and the phrase mining method, current technical themes of significant interest to researchers can be obtained. Next, this paper correlates the multi-source fusion themes through semantic similarity calculation and demonstrates the multi-dimensional technology evolutionary path by using the “theme river map”. Finally, this paper presents an evolution analysis from the perspective of frontier research and technology research, so as to discover the development focus of nanogenerators and predict the future application prospects of nanogenerator technology.
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Shi, Xiaoliang, Xinyue Zhang, Shuaiyu Lu, et al. "Dryland Ecological Restoration Research Dynamics: A Bibliometric Analysis Based on Web of Science Data." Sustainability 14, no. 16 (2022): 9843. http://dx.doi.org/10.3390/su14169843.
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Previous research on ecological restoration mainly includes three fields: water ecology, soil ecology, and atmospheric ecology, and the most abundant is in the field of soil ecology, among which the most abundant is in dryland ecological restoration. Research on dryland ecological restoration is very important in ensuring national food security, ecological security, and preventing a return to poverty. However, the previous research results do not clearly present the interconnection between the huge number of existing dryland ecological restoration studies and do not provide a three-dimensional understanding of the whole picture of dryland ecological restoration research from a broader perspective. Research on dryland ecological restoration has received wide attention from scholars at home and abroad, revealing the international research trends in the current field, which will provide a reference for the theory and practice of future dryland ecological restoration research. Using the SCI-E and SSCI databases of the “Web of Science Core Collection” as sample data sources and using CiteSpace optical measurement software, the 2254 literature in the field of international dryland ecological restoration research were systematically analyzed to track the situation and impact of research in this field by countries around the world, scientific research institutions and significant authors, and to analyze the interdisciplinary and research hotspots in this field, which is of great significance for the follow-up research of dryland ecological restoration. The research results show that: (1) The number of publications in international dryland ecological restoration has increased significantly with years and has strong development potential. (2) Journals representing the research frontier have an intense concentration with various journals. (3) The study of dryland ecological restoration belongs to a highly interdisciplinary discipline, while the two disciplines of ecology and environmental science are the pivot nodes of multidisciplinary disciplines. (4) China’s posts and total citations are among the best, but the average citation is low. (5) Dryland ecological restoration and protection is a hot research field at present, and special attention is paid to the dynamic changes and key driving factors of dryland ecological restoration and the full use of machine learning and extensive data mining to solve complex social-ecological problems. The study recommends that related disciplines must strengthen cooperation in the field of dryland ecological restoration, especially the two disciplines of ecology and environmental science, in order to promote the progress of dryland ecological restoration research theory and practice. China should continue to strengthen the investment of scientific research forces to improve the international influence of research in the field of dryland ecological restoration.
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Geng, Chengwei, Fei Xiong, Yong Liu, et al. "Systematic Exploration of the Knowledge Graph on Rock Porosity Structure." Buildings 15, no. 1 (2024): 101. https://doi.org/10.3390/buildings15010101.
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The porosity structure of rocks is an important research topic in fields such as civil engineering, geology, and petroleum engineering, with significant implications for groundwater flow, oil and gas reservoir exploitation, and geological hazard prediction. This paper systematically explores the research progress and knowledge graph construction methods for rock porosity structure, aiming to provide scientific foundations for a multidimensional understanding and application of rock porosity structure. It outlines the basic concepts and classifications of rock porosity, including the definitions and characteristics of macropores, micropores, and nanopores. This paper provides a comprehensive overview of the main technical methods employed in recent research on rock porosity structure, including X-ray computed tomography, scanning electron microscopy, nuclear magnetic resonance, and 3D reconstruction technologies. It explores the relationship between porosity structure and the physical and mechanical properties of rocks, focusing on the impact of porosity, permeability, and pore morphology on rock mechanical behavior. A knowledge graph of rock porosity structure is constructed to highlight key research areas, core technologies, and emerging applications in this field. The study utilizes extensive literature review and data mining techniques, analyzing 4807 papers published over the past 20 years, sourced from the Web of Science database. Bibliometric and knowledge graph analyses were performed, examining trends such as annual publication volume, country/region distribution, institutional affiliations, journal sources, subject categories, and research databases, as well as research hotspots and frontier developments. This analysis offers valuable insights into the current state of rock porosity structure research, shedding light on its progress and providing references for further advancing research in this area.
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Wang, Yulong, Long Zhang, Guoyan Zhu, et al. "Research Frontiers in Ecological Restoration and Carbon Sequestration in Mining Areas: A Visual Analysis Using VOSviewer." International Journal of Sustainable and Green Energy 13, no. 4 (2024): 90–99. https://doi.org/10.11648/j.ijrse.20241304.13.
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The functioning and progress of modern industrial systems are deeply reliant on mineral resources. While mining offers substantial economic and social gains, it also imposes notable environmental impacts. In the context of global climate change, sustainable mining and ecological restoration in mined areas are increasingly connected to carbon sequestration efforts. Enhancing carbon sink capacity in ecological restoration processes is crucial for achieving carbon neutrality. This study aims to review the current research landscape, identify key research areas, and explore future trends in this field. Relevant literature from the Web of Science was selected, key information extracted, and co-occurrence networks were mapped and analyzed using VOSviewer. Covering publications from 2000 to the present, the analysis spans 84 countries and regions, 1,184 institutions, 3,757 authors, and 858 papers. The main research areas include: (1) strategies for ecological and vegetative restoration of mining areas; (2) carbon sequestration processes in vegetation and soil in mining areas; (3) mechanisms for soil health restoration in mining areas; (4) the role of plants and microbes in pollution remediation; (5) importance of water resource management and wetland restoration in mining areas; and (6) ecological succession and biomass accumulation in mining area rehabilitation. This study highlights major contributors, countries, and institutions, elucidates research hotspots, and outlines directions for future development. By systematically summarizing research trends and hotspots in ecological restoration and carbon sequestration in mining areas, this work provides a valuable reference for researchers seeking to navigate and advance this dynamic field.
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郭, 江丽. "Research on Personal Privacy Data Governance: Hot Spots, Trends and Frontiers." Operations Research and Fuzziology 13, no. 04 (2023): 3072–81. http://dx.doi.org/10.12677/orf.2023.134308.
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Vasumathy, M., Ujjwal Agarwal, G. Divyamrutha, Harikumar Pallathadka, and Dolpriya Devi Manoharmayum. "Exploring Data Mining Applications and Techniques: A Comprehensive Research Survey." International Journal of Membrane Science and Technology 10, no. 3 (2023): 2909–19. http://dx.doi.org/10.15379/ijmst.v10i3.2736.
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Every second, huge amount of data is generated and accumulated. This data could possibly be used in forecasting the future. Data mining uses this data and generates valuable information which can be transformed into relevant knowledge. Data mining is a technique of identifying outliers, behaviours, trends of patterns and relationship among huge datasets. It is hugely associated with the skill of decision making. The knowledge on a relevant subject will help in understanding future trends. This survey paper supplies the overview of data mining, the processes involved, the scope it can offer, its different techniques and multiple applications. Data mining is a great model of using data efficiently.
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Zhang, Yun Bo, Dong Wang, and Jiang Wu. "Research of Data Mining Technology under Tourism Information." Applied Mechanics and Materials 687-691 (November 2014): 1206–9. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1206.
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In this paper, a large amount of raw data has been accumulated in Sanya travel system, and uses the data mining techniques to achieve the Sanya tourism management by the tree algorithm and associated computer research technology. Realizate the interestion of tourist visitors in shopping trends analysis, the model used include regression analysis and trend analysis to analyze behavior characteristics and trends of tourists. It provides scientific support about tourism services management and tourism marketing strategy, and played an important role in Sanya tourism information applications.
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Jindal, Rajni, and Malaya Dutta Borah. "A Survey on Educational Data Mining and Research Trends." International Journal of Database Management Systems 5, no. 3 (2013): 53–73. http://dx.doi.org/10.5121/ijdms.2013.5304.
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Gao, Shang, and Mei Mei Li. "Research of Data Graph Mining Based on Telecommunication Customers." Applied Mechanics and Materials 443 (October 2013): 402–6. http://dx.doi.org/10.4028/www.scientific.net/amm.443.402.
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With the rapid development of the number of mobile phone users has accumulated a large number of graph data, graph data mining has gradually become a hot area of research. Traditional data such as clustering, classification, frequent pattern mining gradually extended to the field of graph data mining research. Introduced at this stage graph data mining technology research progress, summarizes the characteristics of the graphical data mining, practical significance, the main problem, and scenarios to discuss and forecast chart data, especially research on uncertain graph data become trends and hot spots.
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Wu, Jiajing, Dongning Jia, Zhiqiang Wei, and Dou Xin. "Development Trends and Frontiers of Ocean Big Data Research Based on CiteSpace." Water 12, no. 6 (2020): 1560. http://dx.doi.org/10.3390/w12061560.
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Modern socio-economic development and climate prediction depend greatly on the application of ocean big data. With the accelerated development of ocean observation methods and the continuous improvement of the big data science, the challenges of multiple data sources and data diversity have emerged in the ocean field. As a result, the current data magnitude has reached the terabyte scale. Currently, the traditional theoretical foundation and technical methods have their inherent limitations and demerits that cannot satisfied the temporal and spatial attributes of the current ocean big data. Numerous scholars and countries were involved in ocean big data research. To explore the focus and current status, and determine the topics of research on bursts and acquisition of trend related to ocean big data, 400 articles between 1990 and 2019 were collected from the “Web of Science.” Combined with visualization software CiteSpace, bibliometrics method and literature combing technology, the pivotal literature related to ocean big data, including significant level countries, institutions, authors, journals and keywords were recognized. A synthetical analysis has revealed research hot spots and research frontiers. The purpose of this study is to provide researchers and practitioners in the field of ocean big data with the main research domains and research hotspots, and orientation for further research.
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Bayer, Harun, Mustafa Aksogan, Enes Celik, and Adil Kondiloglu. "Big Data Mining and Business Intelligence Trends." Journal of Asian Business Strategy 7, no. 1 (2017): 23–33. http://dx.doi.org/10.18488/journal.1006/2017.7.1/1006.2.23.33.
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The conventional databases are not capable of coping with the high capacity data due to different forms of these data’s and fast production speed. In this context, The Big Data structure comes into the scene. The Big Data has been stated as the gold of our age by many authorities. Today, large sizes of data can be analyzed and this led to changes in the lives of people, companies, states, and researchers. The companies develop effective and efficient solutions by analyzing large size of data through big data solutions for their strategic decisions, operational processes, campaign management and marketing techniques. In this research, the introduction has been made to the Big Data architecture, along with daily increasing data mining techniques and methods which will be a solution for accumulating data and current advancements in big data solutions have been addressed. In addition, some well-known companies’ tendency to implement business intelligence systems have been examined. The effects of potential threads which are the results of the big data in the business world are analyzed and a couple of suggestions for the future have been presented.
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Levkivskyi, Vitalii, Nadiia Lobanchykova, and Dmytro Marchuk. "Research of algorithms of Data Mining." E3S Web of Conferences 166 (2020): 05007. http://dx.doi.org/10.1051/e3sconf/202016605007.
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The article explores data mining algorithms, which based on rules and calculations, that allow us to create a model that analyzes the data provided by searching for specific patterns and trends. The purpose of this work is to analyze correlation-regression algorithms on a statistical dataset of chronic diseases. Data mining allows building many models, multiple algorithms can be used within a single solution. The article explores the algorithms of clustering, correlation analysis, Naive Bayes algorithm for obtaining different views of data. Since diabetes is one of the most dangerous chronic diseases, the pathogenesis of which is a lack of insulin in the human body, which causes metabolic disorders and pathological changes in various organs and tissues. As a result, it leads to disability of all functional systems of the body. It was decided to investigate the data related to this disease. Also, the quality of the developed methods of information retrieval from the dataset was evaluated and the most informative features were identified. The developed methods were implemented in the system of intellectual data processing. Past studies show promise of using data mining methods to improve the quality of patient care.
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Zelenkov, Yury, and Ekaterina Anisichkina. "Trends in data mining research: A two-decade review using topic analysis." Business Informatics 15, no. 1 (2021): 30–46. http://dx.doi.org/10.17323/2587-814x.2021.1.30.46.
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This work analyses the intellectual structure of data mining as a scientific discipline. To do this, we use topic analysis (namely, latent Dirichlet allocation, DLA) applied to the proceedings of the International Conference on Data Mining (ICDM) for 2001–2019. Using this technique, we identified the nine most significant research flows. For each topic, we analyse the dynamics of its popularity (number of publications) and influence (number of citations). The central topic, which unites all other direction, is General Learning, which includes machine learning algorithms. About 20% of the research efforts were spent on the development of this direction for the entire time under review, however, its influence has declined most recently. The analysis also showed that attention to topics such as Pattern Mining (detecting associations) and Segmentation (object separation algorithms such as clustering) is decreasing. At the same time, the popularity of research related to Recommender Systems, Network Analysis, and Human Behaviour Analysis is growing, which is most likely due to the increasing availability of data and the practical value of these topics. The research direction related to practical Applications of data mining also shows a tendency to grow. The last two topics, Text Mining and Data Streams have attracted steady interest from researchers. The results presented here shed light on the structure and trends of data mining over the past twenty years and allow us to expand our understanding of this scientific discipline. We can argue that in the last five years a new research agenda has been formed, which is characterized by a shift in interest from algorithms to practical applications that affect all aspects of human activity.
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Agarwal,, Prakhar, Vinay Pandey, and Dr Bindu Garg. "Survey on Current Trends and Techniques of Data Mining Research." International Journal of Research in Advent Technology 7, no. 4 (2019): 133–37. http://dx.doi.org/10.32622/ijrat.74201920.
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Choi, Jinsu, and Hyewon Chung. "Analysis of Research Trends in Process Data using Text Mining." Journal of Curriculum and Evaluation 27, no. 3 (2024): 197–221. http://dx.doi.org/10.29221/jce.2024.27.3.197.
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Prasad, Bakshi Rohit, and Sonali Agarwal. "Stream Data Mining: Platforms, Algorithms, Performance Evaluators and Research Trends." International Journal of Database Theory and Application 9, no. 9 (2016): 201–18. http://dx.doi.org/10.14257/ijdta.2016.9.9.19.
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Bruno, Francesco, Luigi Palopoli, and Simona E. Rombo. "New Trends in Graph Mining." International Journal of Knowledge Discovery in Bioinformatics 1, no. 1 (2010): 81–99. http://dx.doi.org/10.4018/jkdb.2010100206.
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Searching for repeated features characterizing biological data is fundamental in computational biology. When biological networks are under analysis, the presence of repeated modules across the same network (or several distinct ones) is shown to be very relevant. Indeed, several studies prove that biological networks can be often understood in terms of coalitions of basic repeated building blocks, often referred to as network motifs.This work provides a review of the main techniques proposed for motif extraction from biological networks. In particular, main intrinsic difficulties related to the problem are pointed out, along with solutions proposed in the literature to overcome them. Open challenges and directions for future research are finally discussed.
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J. Jerom Stuward, S. Sri Gugan, and A. Subhashini. "Data Mining for Literary Trends: A Big Data Approach." Shanlax International Journal of English 12, S1-Dec (2023): 167–73. http://dx.doi.org/10.34293/rtdh.v12is1-dec.90.
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In the rapidly evolving landscape of digital humanities, the exploration of literary trends through the lens of big data and data mining methodologies. Traditional approaches to literary analysis have grappled with the sheer volume of textual data, hindering comprehensive examinations across diverse genres and historical periods. Recognizing the transformative potential of big data, these limitations and provide a scalable framework for the nuanced exploration of literary landscapes. On harnessing the power of data mining to uncover overarching trends, stylometric nuances, and thematic evolutions within expansive bodies of literature. Drawing from digital libraries, online platforms, and literary archives, our dataset spans a wide array of genres, authors, and historical epochs. The systematic methodology involves rigorous data pre-processing to ensure quality and consistency, coupled with the application of carefully selected data mining algorithms to extract meaningful patterns. Illustrative case studies form a pivotal part of our investigation, demonstrating the versatility and depth of insights achievable through our big data approach. The interpretative aspects of the findings, unravelling implications for literary studies and criticism. Ethical considerations, including biases in algorithms and responsible data usage, are addressed, underscoring the ethical dimensions of our research. The transformative power of data mining in uncovering literary trends on a scale previously unimaginable. By synthesizing computational methodologies with the richnessof literary expression, our study contributes to the burgeoning field of digital humanities, offering valuable insights into the evolution of language and storytelling.
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Li, Yue, Zhaoying Li, Chunjie Li, et al. "Out-of-hospital cardiac arrest: A data-driven visualization of collaboration, frontier identification, and future trends." Medicine 102, no. 33 (2023): e34783. http://dx.doi.org/10.1097/md.0000000000034783.
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One of the main causes of death is out-of-hospital cardiac arrest (OHCA), which has a poor prognosis and poor neurological outcomes. This phenomenon has attracted increasing attention. However, there is still no published bibliometric analysis of OHCA. This bibliometric analysis of publications on OHCA aimed to visualize the current status of research, determine the frontiers of research, and identify future trends. Publications on OHCA were downloaded from the web of science database. The data elements included year, countries/territories, institutions, authors, journals, research areas, citations of publications, etc. Joinpoint regression and exponential models were used to identify and predict the trend of publications, respectively. Knowledge domain maps were applied to conduct contribution and collaboration, cooccurrence, cocitation, and coupled analyses. Timeline and burst detection analysis were used to identify the frontiers in the field. A total of 3 219 publications on OHCA were found from 1998 to 2022 (average annual percentage change = 16.7; 95% CI 14.4, 19.1). It was estimated that 859 articles and reviews would be published in 2025. The following research hotpots were identified: statement, epidemiology, clinical care, factors influencing prognosis and emergency medical services. The research frontier identification revealed that 7 categories were classified, including therapeutic hypothermia, emergency medical services, airway management, myocardial infarction, extracorporeal cardiopulmonary resuscitation, stroke foundation and trial. The burst detection analysis revealed that percutaneous coronary intervention, neurologic outcome, COVID-19 and extracorporeal cardiopulmonary resuscitation are issues that should be given continual attention in the future. This bibliometric analysis may reflect the current status and future frontiers of OHCA research.
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Ekwe, Prince O., Mathew Okoronkwo, Tochi P. Ukwome, and Valentine U. Anozie. "Multidimensional Data Analysis, Data Mining and Knowledge Discovery." Multidimensional Data Analysis, Data Mining and Knowledge Discovery 9, no. 1 (2024): 6. https://doi.org/10.5281/zenodo.10656236.
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In recent times, the rate of usage and consumption of data has led to the need for these data to be organized, analyzed and used for futuristic prediction and decision making in order to improve human lives and future prediction in different fields of endeavor. Multidimensional Data Analysis, Data Mining and Knowledge Discovery are all associated with the organization, analysis and extraction of a data set for organization’s decision making and futuristic prediction. In this research work, our focus was on the techniques, application of data mining as well as the phases involved in data mining. Our work further highlighted the current and future trends in data mining and the numerous positives of multidimensional data analysis/data mining and knowledge discovery to individuals, organizations, government, societies and the world at large. The outcome of this research would provide a detailed positive view of the impact of data mining to individuals, organizations, government, societies and the world at large for decision making and futuristic prediction. Keywords:- Data, Mining, Data Mining, Multidimensional Data Analysis, Knowledge Data Discovery.
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Miao, Yan, Ying Zhang, and Lihong Yin. "Trends in hepatocellular carcinoma research from 2008 to 2017: a bibliometric analysis." PeerJ 6 (August 15, 2018): e5477. http://dx.doi.org/10.7717/peerj.5477.
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Objectives To comprehensively analyse the global scientific outputs of hepatocellular carcinoma (HCC) research. Methods Data of publications were downloaded from the Web of Science Core Collection. We used CiteSpace IV and Excel 2016 to analyse literature information, including journals, countries/regions, institutes, authors, citation reports and research frontiers. Results Until March 31, 2018, a total of 24,331 papers in HCC research were identified as published between 2008 and 2017. Oncotarget published the most papers. China contributed the most publications and the United States occupied leading positions in H-index value and the number of ESI top papers. Llovet JM owned the highest co-citations. The keyword “transarterial chemoembolization” ranked first in the research front-line. Conclusions The amount of papers published in HCC research has kept increasing since 2008. China showed vast progress in HCC research, but the United States was still the dominant country. Transarterial chemoembolization, epithelial-mesenchymal transition, and cancer stem cell were the latest research frontiers and should be paid more attention.
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Zhou, Ge. "Railway Track Irregularity Data Mining and Time Series Trend Forecasting Research." Applied Mechanics and Materials 666 (October 2014): 272–75. http://dx.doi.org/10.4028/www.scientific.net/amm.666.272.
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Railway track ride is one of the important indicators of the state of the tracks,This article making railway track irregularity data mining,The paper railway track irregularity data mining,data analysis implied regularity and A mathematical model to predict the time-series trends in research,Analysis of the data implied regularity and Build mathematical model getting on time series trend forecasting research.
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Kim, Han-Sol, and Jeong-A. Park. "Analysis of Research Trends in Cosmetology Education using Text Mining." Journal of the Korean Society of Cosmetology 29, no. 3 (2023): 582–92. http://dx.doi.org/10.52660/jksc.2023.29.3.582.
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Identifying academic research trends is an essential task to understand the development patterns of how the discipline has changed in the times and social trends based on the accumulated research results so far, and further establish academic identity. This study was conducted according to the following procedure to analyze the trend of Cosmetology Education Research using text mining. The research procedure proceeded to the stages of data collection, data cleaning, text mining, network analysis, and CONCOR analysis. This study conducted a trend analysis of Cosmetology Education Research using text mining, one of the big data analysis methods, to suggest that the beauty field also needs to respond to changes in the times. Through this, it was confirmed that research on the curriculum and learning satisfaction is most actively conducted, and there is a research variations in the school system and the major. However, as this study has been limited to the field of cosmetology education, it is believed that it can be meaningful as more valuable basic data if big data analysis is conducted by expanding the scope of research.
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Ling, Nie Hui, Chwen Jen Chen, Chee Siong Teh, Dexter Sigan John, Looi Chin Ch’ng, and Yoon Fah Lay. "Global Trends of Educational Data Mining in Online Learning." International Journal of Technology in Education 6, no. 4 (2023): 656–80. http://dx.doi.org/10.46328/ijte.558.
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Educational data mining (EDM) in online learning involves data mining techniques to analyze data from online environments to gain insights into student behavior, performance, and engagement. This study explored EDM in online learning publication trends and focuses. It involved a bibliometric analysis of 615 scholarly works related to EDM in online learning as recorded in Scopus, the largest peer-reviewed citation database, on February 1, 2023. The study examined EDM in online learning publications regarding its evolution and distribution, key focus areas, impact and performance, and prominent authors and collaborations in the last decade, in which the timespan is the period from 2012 to 2022. This bibliometric analysis shows that EDM in online learning is a dynamic area of scientific research as related publications grow steadily throughout the years and involve worldwide collaborations. The study reveals current research trends, offering valuable insights for future researchers to guide their investigations in this field.
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Du, Xu, Juan Yang, Jui-Long Hung, and Brett Shelton. "Educational data mining: a systematic review of research and emerging trends." Information Discovery and Delivery 48, no. 4 (2020): 225–36. http://dx.doi.org/10.1108/idd-09-2019-0070.
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Purpose Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education. Design/methodology/approach This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research. Findings Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward. Research limitations/implications Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals. Originality/value This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.
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Taksa, Isak. "David Taniar: Research and Trends in Data Mining Technologies and Applications." Information Retrieval 11, no. 2 (2008): 165–67. http://dx.doi.org/10.1007/s10791-008-9056-x.
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Das, Subasish, Karen Dixon, Xiaoduan Sun, Anandi Dutta, and Michelle Zupancich. "Trends in Transportation Research." Transportation Research Record: Journal of the Transportation Research Board 2614, no. 1 (2017): 27–38. http://dx.doi.org/10.3141/2614-04.
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Proceedings of journal and conference papers are good sources of big textual data to examine research trends in various branches of science. The contents, usually unstructured in nature, require fast machine-learning algorithms to be deciphered. Exploratory analysis through text mining usually provides the descriptive nature of the contents but lacks quantification of the topics and their correlations. Topic models are algorithms designed to discover the main theme or trend in massive collections of unstructured documents. Through the use of a structural topic model, an extension of latent Dirichlet allocation, this study introduced distinct topic models on the basis of the relative frequencies of the words used in the abstracts of 15,357 TRB compendium papers. With data from 7 years (2008 through 2014) of TRB annual meeting compendium papers, the 20 most dominant topics emerged from a bag of 4 million words. The findings of this study contributed to the understanding of topical trends in the complex and evolving field of transportation engineering research.
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Hao, T., and C. Weng. "Adaptive Semantic Tag Mining from Heterogeneous Clinical Research Texts." Methods of Information in Medicine 54, no. 02 (2015): 164–70. http://dx.doi.org/10.3414/me13-01-0130.
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SummaryObjectives: To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts.Methods: We develop a “plug-n-play” framework that integrates replaceable un-supervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach’s recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach’s adaptability to two other types of clinical research texts: clinical data requests and clinical trial protocols, by comparing the prevalence trends of FSTs across three texts.Results: Our approach increased the average recall and speed by 12.8% and 47.02% respectively upon the baseline when mining FSTs from ClinicalTrials.gov, and maintained an overlap in relevant FSTs with the baseline ranging between 76.9% and 100% for varying FST frequency thresholds. The FSTs saturated when the data size reached 200 documents. Consistent trends in the prevalence of FST were observed across the three texts as the data size or frequency threshold changed.Conclusions: This paper contributes an adaptive tag-mining framework that is scalable and adaptable without sacrificing its recall. This component-based architectural design can be potentially generalizable to improve the adaptability of other clinical text mining methods.
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Cho, Nahye, and Youngok Kang. "A Research Trends about Spatio-temporal Data Mining and Visualization of Log Data." Journal of the Korean Cartographic Association 16, no. 3 (2016): 15–27. http://dx.doi.org/10.16879/jkca.2016.16.3.015.
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Wang, Yinying. "Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges." education policy analysis archives 25 (August 28, 2017): 94. http://dx.doi.org/10.14507/epaa.25.3037.
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Despite abundant data and increasing data availability brought by technological advances, there has been very limited education policy studies that have capitalized on big data—characterized by large volume, wide variety, and high velocity. Drawing on the recent progress of using big data in public policy and computational social science research, this commentary discusses how to approach big data and how big data can be used in education policy research. First, I introduce big data that is potentially relevant to education policy research. I then present methodological frontiers by examining the assumptions, key concepts, merits, and caveats of three commonly used analytical approaches to mining massive amounts of text data: topic models, network text analysis, and sentiment analysis. Next, to ensure the veracity of using big data in education policy research, I debunk three methodological misconceptions. This commentary concludes with a discussion on developing interdisciplinary research capacity and addressing the privacy concerns and ethical conundrums as we explore a research agenda of using big data in education policy.
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Li, Baochan, Anan Pongtornkulpanich, and Thitinan Chankoson. "Knowledge Mapping to Understand Corporate Value: Literature Review and Bibliometrics." Journal of Risk and Financial Management 17, no. 2 (2024): 42. http://dx.doi.org/10.3390/jrfm17020042.
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The purpose of this study is to summarize the research results on corporate value published from 2000 to 2022; show the research overview, hot trends, and topic evolution of this research field; provide new ideas for the mining of the research frontiers of corporate value and a summary of the change rules of research hotspots; and describe prospects for the evolution direction and path of future research. Combining the bibliometric research method with a literature review, the research results on corporate value were analyzed quantitatively by querying the WOS database from 2000 to 2022; the analysis tool was CiteSpace. This study has five findings. First, researchers are paying increasing attention to the study of corporate value, and most of the research results are obtained by independent authors. Second, Chinese research institutions rank among the top three in publication volume. However, their research results have had little impact, with Univ Penn and Peking Univ having the most significant impact. Third, the top three keywords that scholars pay attention to are performance, impact, and corporate governance. Keyword burst analysis, CSR, value reliability, and sustainability are the latest research frontiers. Fourth, evolutionary trends are divided into three stages: research on the influencing factors of corporate value, research on the impact of corporate behavior on corporate value, and research on the evaluation and growth of corporate value. Fifth, knowledge domains include corporate value research methods, the factors influencing corporate value, and corporate behavior. The aims of this study are to provide a new perspective for researchers to study corporate value, provide new ideas for enterprise managers to manage corporate value, and achieve the sustainable development of corporate value. At the same time, the scientific knowledge graph method is applied in corporate value research, adding a new research path for corporate value.
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Baker, Ryan S.J.d., and Kalina Yacef. "The State of Educational Data Mining in 2009: A Review and Future Visions." Journal of Educational Data Mining 1, no. 1 (2009): 3–17. https://doi.org/10.5281/zenodo.3554658.
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We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries ("discovery with models"), and the reduction in the frequency of relationship mining within the EDM community. We discuss two ways that researchers have attempted to categorize the diversity of research in educational data mining research, and review the types of research problems that these methods have been used to address. The most cited papers in EDM between 1995 and 2005 are listed, and their influence on the EDM community (and beyond the EDM community) is discussed.
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Khin, Sein Hlaing, and Myo Kay Khine Thaw Yin. "Applications, Techniques and Trends of Data Mining and Knowledge Discovery Database." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1604–6. https://doi.org/10.5281/zenodo.3591147.
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Data Mining and Knowledge Discovery is intended to be the best technical publication in the field providing a resource collecting relevant common methods and techniques. Traditionally, data mining and knowledge discovery was performed manually. As time passed, the amount of data in many systems grew to larger than terabyte size, and could no longer be maintained manually. Besides, for the successful existence of any business, discovering underlying patterns in data is considered essential. This paper proposed about applications, techniques and trends of Data Mining and Knowledge Discovery Database. Khin Sein Hlaing | Yin Myo Kay Khine Thaw "Applications, Techniques and Trends of Data Mining and Knowledge Discovery Database" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26733.pdf
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H., Ramprasanth, and M. Shanmugapriya Dr. "SURVEY ON DATA MINING FOR WEB SEMANTIC ANALYSIS." IJRSET JUNE Volume 9 Issue 6 9, no. 6 (2022): 23–27. https://doi.org/10.5281/zenodo.6793309.
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Data mining plays an important role in various human activities because it extracts the unknown useful patterns (or knowledge). Due to its capabilities, data mining become an essential task in large number of application domains such as banking, retail, medical, insurance, bioinformatics, etc. To take a holistic view of the research trends in the area of data mining, a comprehensive survey is presented in this paper. This paper presents a systematic and comprehensive survey of various data mining tasks and techniques. Further, various real-life applications of data mining are presented in this paper. The challenges and issues in area of data mining research are also presented in this paper. The main objective of the paper is to analyze and compare different data mining techniques used in the medical applications. We present a summary of the results and provide comparison analysis of the data mining methods employed by the reviewed articles.
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Du, Chunjing, Yi Zhang, Hanwen Zhang, Hua Zhang, Jingyuan Liu, and Ning Shen. "Bibliometric Analysis of Research Trends and Prospective Directions of Lung Microbiome." Pathogens 13, no. 11 (2024): 996. http://dx.doi.org/10.3390/pathogens13110996.
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The lung microbiome has emerged as a pivotal area of research in human health. Despite the increasing number of publications, there is a lack of research that comprehensively and objectively presents the current status of lung microbiome-related studies. Thus, this study aims to address this gap by examining over two decades of publications through bibliometric analysis. The original bibliographic data of this study were obtained from the Web of Science Core Collection, focusing on publications from 2003 to 2023. The analysis included the data extraction and examination of authors, affiliations, countries, institutions, abstracts, keywords, references, publication dates, journals, citations, H-indexes, and journal impact factors. A total of 845 publications were identified, showing an increasing trend in both publications and citations over the years, particularly in the last decade. The analysis highlighted the most productive authors, institutions, and countries/regions, and identified potential partners for interested researchers. Co-citation analysis revealed that lung microbiome- and infectious/pulmonary disease-related studies are at the forefront of the field. The hotspots and frontiers of the lung microbiome field have progressed from basic composition to exploring specific mechanisms and the clinical value of diseases. In conclusion, this study provides a comprehensive overview of the current research status and trends in the field of the lung microbiome over the past two decades and highlights the areas that need more attention and research efforts. It offers valuable insights for researchers and institutions and identifies key hotspots and frontiers, which can serve as references for related researchers and future research.
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Park, Shinjae. "Trend Analysis of Fluency-Related Research Articles: Using Data Mining." Forum for Linguistic Studies 6, no. 4 (2024): 449–62. http://dx.doi.org/10.30564/fls.v6i4.6797.
Texte intégralRésumé :
This research seeks to identify recent research trends in analysing fluency in English language studies since 2010. The importance of studying corpora that compile registered research in linguistics is growing; thus, it is time to consider the significance of data mining analysis on the corpora. Considering this emerging research topic, this analysis intends to generate and compare word clouds and word metric charts from speaking and writing abstract corpora in linguistics research centred on the keyword ‘Fluency’. This comparison is minimally addressed in extant research; hence, this investigation aims to fill this gap. The corpus contains 50 speaking and 15 writing abstracts from linguistics journals. To create the word cloud, AntConc4.1.4 software was used to analyse the corpora, while TF-IDF and matrix analysis were conducted using R. As a result, in spoken English, ‘fluency’ studies were largely related to 'teaching' and tended to be dominated by studies focusing on ‘fluency’ alone. In contrast, writing studies were mostly related to learner proficiency and assessment, and many studies analysed the relationship between linguistic abilities by expanding the scope of ‘fluency’ to include ‘accuracy’ and ‘complexity’. This study will benefit researchers in deciding on topics as it provides research directions and trends in analysing fluency in the two interconnected but differently expressed fields (i.e., speaking and writing).