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Internet of things:Conceptual network structure,main challenges and future directions

Digital Communications and Networks 2023年第3期9卷 677-687页

作者： Leonardo B.Furstenau Yan Pablo Reckziegel Rodrigues Michele Kremer Sott Pedro Leivas Michael S.Dohan José Ricardo López-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Department of Industrial Engineering Federal University of Rio Grande do SulPorto Alegre 90035-190Brazil University of Santa Cruz do Sul Av.Independencia2293Santa Cruz do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Lakehead University Thunder BayONP7B 5E1Canada Academic Unit of Accounting and Management Autonomous University of ZacatecasCalle Comercio y Administracion S/NFracc.ProgresoC.P.98066ZacatecasZacatecasMexico Department of Computer science and Artificial Intelligence University of Granada18071 GranadaSpain York University ONM3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Internet of Things(IoT)is a key technology trend that supports our digitalized society in applications such as smart countries and smart cities.In this study,we investigate the existing strategic themes,thematic evolution structure,key challenges,and potential research opportunities associated with the IoT.For this study,we conduct a Bibliometric Performance and Network Analysis(BPNA),supplemented by an exhaustive Systematic Literature Review(SLR).Specifically,in BPNA,the software SciMAT is used to analyze 14,385 documents and 30,381 keywords in the Web of science(WoS)database,which was released between 2002 and 2019.The results reveal that 31 clusters are classified according to their importance and development,and the conceptual structures of key clusters are presented,along with their performance analysis and the relationship with other subthemes.The thematic evolution structure describes the important cluster(s)over time.For the SLR,23 documents are analyzed.The SLR reveals key challenges and limitations associated with the IoT.We expect the results will form the basis of future research and guide decision-making in the IoT and other supporting industries.

关键词： Internet of things Strategic intelligence Industry 4.0 SciMAT Bibliometric analysis science mapping

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Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

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Journal of Data and Information science 2021年第3期6卷 99-122页

作者： Sahand Vahidnia Alireza Abbasi Hussein A.Abbass University of New South Wales Canberra2612ACTAustralia

Purpose:Detection of research fields or topics and understanding the dynamics help the scientific community in their decisions regarding the establishment of scientific fields.This also helps in having a better collaboration with governments and businesses.This study aims to investigate the development of research fields over time,translating it into a topic detection problem.Design/methodology/approach:To achieve the objectives,we propose a modified deep clustering method to detect research trends from the abstracts and titles of academic documents.Document embedding approaches are utilized to transform documents into vector-based representations.The proposed method is evaluated by comparing it with a combination of different embedding and clustering approaches and the classical topic modeling algorithms(i.e.LDA)against a benchmark dataset.A case study is also conducted exploring the evolution of Artificial Intelligence(AI)detecting the research topics or sub-fields in related AI publications.Findings:Evaluating the performance of the proposed method using clustering performance indicators reflects that our proposed method outperforms similar approaches against the benchmark dataset.Using the proposed method,we also show how the topics have evolved in the period of the recent 30 years,taking advantage of a keyword extraction method for cluster tagging and labeling,demonstrating the context of the topics.Research limitations:We noticed that it is not possible to generalize one solution for all downstream tasks.Hence,it is required to fine-tune or optimize the solutions for each task and even datasets.In addition,interpretation of cluster labels can be subjective and vary based on the readers’opinions.It is also very difficult to evaluate the labeling techniques,rendering the explanation of the clusters further limited.Practical implications:As demonstrated in the case study,we show that in a real-world example,how the proposed method would enable the researchers and reviewers of the academic research to detect,su

关键词： Dynamics of science science mapping Document clustering Artificial intelligence Deep learning

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Big data in education:Themes and trends

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Data science and Informetrics 2023年第1期3卷 26-36页

作者： Teng Zhao Shiji Chen Junping Qiu Yunlong Yu Zhejiang Academy of Higher Education Hangzhou Dianzi UniversityHangzhouChina Chinese Academy of science and Education Evaluation Hangzhou Dianzi UniversityHangzhouChina Academy of Data science and Informetrics Hangzhou Dianzi UniversityHangzhouChina

With the rapid growth of big data research,the existing research has investigated the research themes and trends of big data in different disciplines,but less has paid attentions on the field of education.Using bibliographic data from the Web of science(Wos),we conduct bibliometric analysis and science mapping to explore the research themes and trends of big data in education.The results show that though education is not the major producer of big data research,it does have a positive development trend.In addition,we find that big data in education mainly serves as a tool to facilitate educational outcomes.Implications,limitations,and futuredirections arediscussed.

关键词： Big data Education Themes and trends Bibliometric analysis science mapping

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Using Network Embedding to Obtain a Richer and More Stable Network Layout for a Large Scale Bibliometric Network

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Journal of Data and Information science 2021年第1期6卷 154-177页

作者： Ting Chen Guopeng Li Qiping Deng Xiaomei Wang National science Library Chinese Academy of SciencesBeijing 100190China Department of Library Information and Archives ManagementUniversity of Chinese Academy of SciencesBeijing 100049China Institutes of science and Development Chinese Academy of SciencesBeijing 100190China Library University of Electronic Science and Technology of ChinaChengdu 611731China

Purpose: The goal of this study is to explore whether deep learning based embed ded models can provide a better visualization solution for large citation networks. De sign/methodology/approach: Our team compared the visualization approach borrowed from the deep learning community with the well-known bibliometric network visualization for large scale data. 47,294 highly cited papers were visualized by using three network embedding models plus the t-SNE dimensionality reduction technique. Besides, three base maps were created with the same dataset for evaluation purposes. All base maps used the classic Open Ord method with different edge cutting strategies and parameters. Findings: The network embedded maps with t-SNE preserve a very similar global structure to the full edges classic force-directed map, while the maps vary in local structure. Among them, the Node2Vec model has the best overall visualization performance, the local structure has been significantly improved and the maps' layout has very high stability.Research limitations: The computational and time costs of training are very high for network em bedded models to obtain high dimensional latent vector. Only one dimensionality reduction technique was tested. Practical implications: This paper demonstrates that the network embedding models are able to accurately reconstruct the large bibliometric network in the vector space. In the future, apart from network visualization, many classical vector-based machine learning algorithms can be applied to network representations for solving bibliomet ric analysis tasks. Originality/value: This paper provides the first systematic comparison of classical science mapping visualization with network embedding based visualization on a large scale dataset. We showed deep learning based network embedding model with t-SNE can provide a richer,more stable science map. We also designed a practical evaluation method to investigate and compare maps.

关键词： Scientometrics Visualization Essential science indicators Bibliometric networks Network embedding science mapping

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mapping scientific cooperation of the NSFC:A case study of Major International(Regional) Joint Research Program

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Chinese Journal of Library and Information science 2011年第1期4卷 1-14页

作者： Qingfeng DUAN Xuefeng WANG School of Economics and Management North University of China School of Management and Economics Beijing Institute of Technology

International cooperations have played an important role in academic activities as more and more researchers endeavor to participate in joint research or exchange with partners aboard. Focused on projects of Major International(Regional) Joint Research Program of the Natural science Foundation of China(NSFC) from 2003 to 2008, we have investigated the characteristics of the scientific cooperation by mapping the funded projects from the aspects of topics, affiliations and cooperative countries(regions), respectively.Moreover, it is demonstrated that issues related to nano technologies, genes and ecological environments have become the hot topics in the joint research of NSFC, and projects with issues of big science have been preferentially funded under the program’s guide posted every year. Moreover, most granted projects were undertaken by the minority of affiliations with the leading research capability, which is the same to partners abroad. The aggregation effect has been greatly improved with the bilateral or multilateral agreement signed with scientific organizations abroad, so has the competitive mechanism of scientific resources allocation.

关键词： Scientific cooperation International cooperation National Natural science Foundation of China(NSFC) science mapping

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