Journal articles on the topic 'Data Mining Trends and Research Frontiers'
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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 (May 21, 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 (July 7, 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 (August 9, 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 (September 1, 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 (March 28, 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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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 (August 1, 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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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 (January 13, 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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Borgman, Christine L. "Whose text, whose mining, and to whose benefit?" Quantitative Science Studies 1, no. 3 (September 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 (August 22, 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, Yujie Qiang, Jie Kong, Rui Huang, Haining Wang, and Hui Liu. "Visualizing the Knowledge Base and Research Hotspot of Public Health Emergency Management: A Science Mapping Analysis-Based Study." Sustainability 14, no. 12 (June 16, 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 (September 16, 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 (June 11, 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, Tielong Wang, Jiayi Zhang, Yuanpeng Liang, and Jifeng Deng. "Dryland Ecological Restoration Research Dynamics: A Bibliometric Analysis Based on Web of Science Data." Sustainability 14, no. 16 (August 9, 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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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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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 (June 30, 2013): 53–73. http://dx.doi.org/10.5121/ijdms.2013.5304.
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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 (October 17, 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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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 (May 29, 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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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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Bayer, Harun, Mustafa Aksogan, Enes Celik, and Adil Kondiloglu. "Big Data Mining and Business Intelligence Trends." Journal of Asian Business Strategy 7, no. 1 (November 8, 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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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 (April 10, 2019): 133–37. http://dx.doi.org/10.32622/ijrat.74201920.
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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 (September 30, 2016): 201–18. http://dx.doi.org/10.14257/ijdta.2016.9.9.19.
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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 (March 31, 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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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 (December 14, 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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Bruno, Francesco, Luigi Palopoli, and Simona E. Rombo. "New Trends in Graph Mining." International Journal of Knowledge Discovery in Bioinformatics 1, no. 1 (January 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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Li, Yue, Zhaoying Li, Chunjie Li, Wei Cai, Tao Liu, Ji Li, Haojun Fan, and Chunxia Cao. "Out-of-hospital cardiac arrest: A data-driven visualization of collaboration, frontier identification, and future trends." Medicine 102, no. 33 (August 18, 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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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 (May 18, 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 (February 26, 2008): 165–67. http://dx.doi.org/10.1007/s10791-008-9056-x.
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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 (October 23, 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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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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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 (January 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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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 (December 30, 2016): 15–27. http://dx.doi.org/10.16879/jkca.2016.16.3.015.
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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 (June 30, 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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Gülkesen, Kemal Hakan, and Reinhold Haux. "Research Subjects and Research Trends in Medical Informatics." Methods of Information in Medicine 58, S 01 (March 27, 2019): e1-e13. http://dx.doi.org/10.1055/s-0039-1681107.
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Objectives To identify major research subjects and trends in medical informatics research based on the current set of core medical informatics journals. Methods Analyzing journals in the Web of Science (WoS) medical informatics category together with related categories from the years 2013 to 2017 by using a smart local moving algorithm as a clustering method for identifying the core set of journals. Text mining analysis with binary counting of abstracts from these journals published in the years 2006 to 2017 for identifying major research subjects. Building clusters based on these terms for the complete time period as well as for the periods 2006–2008, 2009–2011, 2012–2014, and 2015–2017 for identifying trends. Results The identified cluster includes 17 core medical informatics journals. By text mining of these journals, 224,992 different terms in 14,414 articles were identified covering 550 specific key terms. Based on these key terms five clusters were identified: “Biomedical Data Analysis,” “Clinical Informatics,” “EHR and Knowledge Representation,” “Mobile Health,” and “Organizational Aspects of Health Information Systems.” No shifts in the clusters were observed between the first two 3-year periods. In the third period, some terms like “mobile phone,” “mobile apps,” and “message” appear. Also, in the third period, a “Clinical Informatics” cluster appears and persists in the fourth period. In the fourth period, a rearrangement of clusters was observed. Conclusions Beside classical subjects of medical informatics on organizing, representing, and analyzing data, we observed new developments in the context of mobile health and clinical informatics. These subjects tended to grow over the past years, and we can expect this trend to continue.
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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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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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P. Dinesh kumar, Dr. B. Subramani. "Stock Market Data Using Data Mining For Feature Extraction." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (October 26, 2023): 2062–70. http://dx.doi.org/10.52783/tjjpt.v44.i4.1181.
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This paper presents a robust approach for feature extraction from stock market data by combining Principal Component Analysis (IPCA) and Moving Averages (MA). IPCA reduces dimensionality, capturing underlying patterns, while MAs identify trends and cyclic behaviors. The synergistic integration of these techniques enhances the extraction of essential features for stock market analysis. Research method effectively uncovers relevant information, offering valuable insights for trading and investment decisions. It addresses dimensionality challenges and identifies meaningful patterns, promoting a deeper understanding of market dynamics.
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Huancheng, Liu, Wu Tingting, and Álvaro Rocha. "An Analysis of Research Trends on Data Mining in Chinese Academic Libraries." Journal of Grid Computing 17, no. 3 (August 8, 2018): 591–601. http://dx.doi.org/10.1007/s10723-018-9461-3.
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Kim, Minjun. "Identifying Research Trends in Big data-driven Digital Transformation Using Text Mining." Korean Institute of Smart Media 11, no. 10 (November 30, 2022): 54–64. http://dx.doi.org/10.30693/smj.2022.11.10.54.
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A big data-driven digital transformation is defined as a process that aims to innovate companies by triggering significant changes to their capabilities and designs through the use of big data and various technologies. For a successful big data-driven digital transformation, reviewing related literature, which enhances the understanding of research statuses and the identification of key research topics and relationships among key topics, is necessary. However, understanding and describing literature is challenging, considering its volume and variety. Establishing a common ground for central concepts is essential for science. To clarify key research topics on the big data-driven digital transformation, we carry out a comprehensive literature review by performing text mining of 439 articles. Text mining is applied to learn and identify specific topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview. A total of 10 key research topics and relationships among the topics are identified. This study contributes to clarifying a systematized view of dispersed studies on big data-driven digital transformation across multiple disciplines and encourages further academic discussions and industrial transformation.
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Sawale, Arti. "Text Mining of Twitter data for Mapping the Digital Humanities Research Trends." DESIDOC Journal of Library & Information Technology 43, no. 04 (August 1, 2023): 258–65. http://dx.doi.org/10.14429/djlit.43.04.19236.
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Digital humanities have become a more relevant field of study due to the extraordinary growth in digitisation of the humanities data. Due to collaborative development of humanities and computing, many academics are convinced of the worth of digital humanities (DH) that actually provides the best insight into humanities studies. The panoramic view of the development of big data in humanities reflects its trendy directions and evoked new challenges in DH. It is complicated to analysed the objectives of digital humanities data with simple data analysis tools where as text mining can help to facilitate the qualitative findings in DH. In the humanities disciplines, data is often in the form of unstructured and text mining is a way of structuring and analysing digitised text-as-data. Twitter is a online social networking platform which offers an opportunity for quality information sharing, collaborative participation digital humanities community. This paper is attempted to study the extensibility of digital humanities on twitter and also to interpret the evolution of twitter usage by analysing tweets posted related to DH via python data analysis.
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Lee, Hyo Seong, Hae Goo Song, and Hee Sang Lee. "Classification of Photovoltaic Research Papers by Using Text-Mining Techniques." Applied Mechanics and Materials 284-287 (January 2013): 3362–69. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3362.
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The research described in this article focuses on one important aspect of monitoring scientific and technological trends and tries to examine topics of research and trends in the photovoltaic field. The data used to examine the research and trends were scientific and technological literature published during the last five years, which were exhaustively collected from the two SCI journals that specialize in photovoltaic and solar energy research. In order to analyze the 2,031 academic papers colllected, text-mining was applied. As a result, research topics were identified through document clustering and classified through text categorization into four major subjects; ‘Cell’, ‘Module/Array’, ‘System’ and ‘Relative/Advanced.’
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Csizmazia, Roland Attila. "Identifying Automotive Industry Trends: Data Mining from Intellectual Property Databases." Journal of Business and Management Studies 3, no. 2 (September 7, 2021): 120–24. http://dx.doi.org/10.32996/jbms.2021.3.2.12.
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Patents protect the patent holders in that the patented process, design and invention can only be used or sold by the holder exclusively. Therefore, manufacturers use patents to gain a competitive edge against the competition. A patent analysis discovers which car parts are considered the most for future development in the automotive sector. The purpose of this paper is to identify and analyze major trends and the potential implementation of patents. Furthermore, the research may reveal in detail the common R&D trends within a certain industry, differences among the major representative manufacturers and support to identify feasible future strategies for lagers. The patent analysis will be launched with the data collection from a patent database. To avoid the extensive computing time in R, only each patent document's abstract is deployed for the research. After data cleansing, the term frequency-inverse document frequency algorithm is used to find the keywords in the patent abstracts. To visualize results, the social network analysis is conducted. It identifies trends and relationships among the mapped keywords. The discovered major keywords constitute the graphs of the most important parts related to each other and are considered for the future.
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Du, Weifeng, Zhoutong Wu, Huaiju Wu, Yanlei Li, and Yebin Jin. "Global trends and frontiers of research on Kümmell’s disease: A bibliometric analysis." Medicine 103, no. 27 (July 5, 2024): e38833. http://dx.doi.org/10.1097/md.0000000000038833.
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The prevalence of Kümmell’s disease (KD) has been increasing due to the aging population and the rise of osteoporotic vertebral compressibility fractures. As a result, there has been a growing concern about this condition. Despite the rapid advancements in its related research fields, the current research status and hotspot analysis of KD remain unclear. Therefore, our goal was to identify and analyze the global research trends on KD using bibliometric tools. All KD data were obtained from the Web of Science Core Collection. The information of research field was collected, including title, author, institutions, journals, countries, references, total citations, and years of publication for further analysis. From 1900 to 2022, a total of 195 articles and 1973 references have been published in this field, originating from 27 countries/regions and 90 journals, with China leading the contributions. The most significant institutional and author contributions come from Soochow University and Kim, HS, respectively. The journal with the highest number of published research and total citation frequency is Spine. The latest research focuses in this field include “risk factor,” “osteoporotic vertebral compression fracture,” “pedicle screw fixation,” “percutaneous vertebroplasty,” and “bone cement,” and should be closely monitored. Additionally, we have conducted a comprehensive analysis of the 50 most-cited articles in KD, providing a valuable list of articles to guide clinical decision-making and future research for clinicians and researchers. In recent years, there has been a significant increase in scientific research on KD. Future research in KD is likely to focus on surgical treatment, risk factors, and complications.
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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 (January 23, 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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Savla, Jyoti, Karen Roberto, and Mamta Sapra. "New Frontiers in Caregiving Research: Biopsychosocial Perspectives and Interventions." Innovation in Aging 4, Supplement_1 (December 1, 2020): 615. http://dx.doi.org/10.1093/geroni/igaa057.2086.
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Abstract Although families embrace the opportunity to care for a loved one, caregiving is stressful and takes a toll on the caregiver’s health and well-being. Earlier studies of stress and coping among family caregivers focused on psychological outcomes and emotional well-being. In the last decade, stress researchers have broadened their focus to include biomarkers and health outcomes. Data from two studies of caregivers of persons with memory loss will be used to discuss two new frontiers of caregiving research. First, a daily-diary study will be used to identify the mechanism by which stress disrupts the physiological processes and proliferates into serious psychopathology and pre-clinical and clinical health conditions. Second, a mindfulness-based psychoeducational intervention study will be utilized to identify malleable factors that can be harnessed to lower stress and improve the well-being of family caregivers. Next steps for caregiving research in the context of demographic and technological trends will be discussed.