An Insight into the State of Big Data Research: A Bibliometric Study of Scientific Publications

References

• Aboelmaged, M., and S. Mouakket. 2020. Influencing models and determinants in big data analytics research: A bibliometric analysis. Information Processing & Management 57 (4):102234. doi:10.1016/j.ipm.2020.102234.
   Web of Science ®Google Scholar
• Agustí, M. A., and M. Orta-Pérez. 2022. Big data and artificial intelligence in the fields of accounting and auditing: A bibliometric analysis. Spanish Journal of Finance and Accounting / Revista Española de Financiación y Contabilidad 1–27. doi:10.1080/02102412.2022.2099675.
   Web of Science ®Google Scholar
• Ahmad, I., G. Ahmed, S. Adeel Ali Shah, and E. Ahmed. 2020. A decade of big data literature: Analysis of trends in light of bibliometrics. The Journal of Supercomputing 76 (5):3555–71. doi:10.1007/s11227-018-2714-x.
   Web of Science ®Google Scholar
• Akoka, J., I. Comyn-Wattiau, and N. Laoufi. 2017. Research on big data – a systematic mapping study. Computer Standards & Interfaces 54 (November):105–15. doi:10.1016/j.csi.2017.01.004.
   Web of Science ®Google Scholar
• Alemany Oliver, M., and J. -S. Vayre. 2015. Big data and the future of knowledge production in marketing research: Ethics, digital traces, and abductive reasoning. Journal of Marketing Analytics 3 (1):5–13. doi:10.1057/jma.2015.1.
   Google Scholar
• Al-Fuqaha, A., M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash. 2015. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials 17 (4):2347–76. doi:10.1109/COMST.2015.2444095.
   Web of Science ®Google Scholar
• Ardito, L., V. Scuotto, M. Del Giudice, and A. M. Petruzzelli. 2019. A bibliometric analysis of research on big data analytics for business and management. Management Decision 57 (8):1993–2009. doi:10.1108/MD-07-2018-0754.
   Web of Science ®Google Scholar
• Aria, M., and C. Cuccurullo. 2017. Bibliometrix: An R-Tool for comprehensive science mapping analysis. Journal of Informetrics 11 (4):959–75. doi:10.1016/j.joi.2017.08.007.
   Web of Science ®Google Scholar
• Arief, N. N., and A. Gustomo. 2020. Analyzing the impact of big data and artificial intelligence on the communications profession: A case study on public relations (PR) practitioners in Indonesia. International Journal on Advanced Science, Engineering and Information Technology 10 (3):1066–71. doi:10.18517/ijaseit.10.3.11821.
   Google Scholar
• Aykroyd, R. G., V. Leiva, and F. Ruggeri. 2019. Recent developments of control charts, identification of big data sources and future trends of current research. Technological Forecasting and Social Change 144 (July):221–32. doi:10.1016/j.techfore.2019.01.005.
   Web of Science ®Google Scholar
• Bąba, W., A. Kompała-Bąba, M. Zabochnicka-Świątek, J. Luźniak, R. Hanczaruk, A. Adamski, and H. M. Kalaji. 2019. Discovering trends in photosynthesis using modern analytical tools: More than 100 reasons to use chlorophyll fluorescence. Photosynthetica 57 (2):668–79. doi:10.32615/ps.2019.069.
   Web of Science ®Google Scholar
• Batistič, S., and P. der Laken. 2019. History, evolution and future of big data and analytics: A bibliometric analysis of its relationship to performance in organizations. British Journal of Management 30 (2):229–51. doi:10.1111/1467-8551.12340.
   Web of Science ®Google Scholar
• Botta, A., W. de Donato, V. Persico, and A. Pescapé. 2016. Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems 56:684–700. doi:10.1016/j.future.2015.09.021.
   Web of Science ®Google Scholar
• Boyd, D., and K. Crawford. 2012. CRITICAL QUESTIONS for BIG DATA: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society 15 (5):662–79. doi:10.1080/1369118X.2012.678878.
   Web of Science ®Google Scholar
• Capetillo, A., D. Camacho, and M. Alanis. 2022. Blockchained education: Challenging the long-standing model of academic institutions. International Journal on Interactive Design and Manufacturing 16 (2):791–802. doi:10.1007/s12008-022-00886-1.
   Google Scholar
• Cardona-Acevedo, S., W. Londoño Celis, J. Quiroz Fabra, and A. Valencia-Arias. 2022. Research trends on the role of big data in artificial intelligence: A bibliometric analysis. Studies in Computational Intelligence 1061:121–34. doi:10.1007/978-3-031-14748-7_7.
   Google Scholar
• Ceci, M., S. Ferilli, and A. Poggi, ed. 2020. Digital libraries: The Era of big data and data science. In Communications in Computer and Information Science, Vol. 1177. Cham: Springer International Publishing. doi:10.1007/978-3-030-39905-4.
   Google Scholar
• Chavez, H., M. Belén Albornoz, and F. Martín. 2022. ‘Big data’ research: A Bibliometric analysis of the Scopus database, 2009–2019. Journal of Scientometric Research 11 (1):64–78. doi:10.5530/jscires.11.1.7.
   Web of Science ®Google Scholar
• Chen, C., and S. 2012. Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly 36 (4):1165. doi:10.2307/41703503.
   Web of Science ®Google Scholar
• Chen, D. 2022. Statistical analysis of green building research hotspots based on bibliometrics big data and cloud computing. 396–99. doi:10.1109/EEBDA53927.2022.9744885.
   Google Scholar
• Chen, S.C., R. Jain, Y. Tian, and H. Wang. 2015. Guest editorial multimedia: The biggest big data. IEEE Transactions on Multimedia 17 (9):1401–03. doi:10.1109/TMM.2015.2459331.
   Web of Science ®Google Scholar
• Chen, M., S. Mao, and Y. Liu. 2014. Big Data: A Survey. Mobile Networks and Applications 19 (2):171–209. doi:10.1007/s11036-013-0489-0.
   Web of Science ®Google Scholar
• Chunlei, Y. 2018. Bibliometrical analysis of international big data research: Based on citespace and VOSviewer. In 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 927–32. Huangshan, China: IEEE. doi:10.1109/FSKD.2018.8687153.
   Google Scholar
• Cox, M., and D. Ellsworth. 1997. Managing big data for scientific visualization. January.
   Google Scholar
• Dai, Z., X. Simin, W. Xue, H. Ruixue, L. Huimin, H. Haoqiang, H. Jing, and X. Liao. 2022. Knowledge mapping of multicriteria decision analysis in healthcare: A bibliometric analysis. Frontiers in Public Health 10 (June):895552. doi:10.3389/fpubh.2022.895552.
   PubMed Web of Science ®Google Scholar
• Dogo, E. M., T. Makaba, O. J. Afolabi, and A. C. Ajibo. 2021. Combating road traffic congestion with big data: A bibliometric review and analysis of scientific research. In Towards connected and autonomous vehicle highways EAI/Springer innovations in communication and computing, ed. U. Z. A. Hamid and F. Al-Turjman, 43–86. Cham: Springer International Publishing. doi:10.1007/978-3-030-66042-0_4.
   Google Scholar
• El-Alfy, E.S.M., and S. A. Mohammed. 2020. A review of machine learning for big data analytics: Bibliometric approach. Technology Analysis & Strategic Management 32 (8):984–1005. doi:10.1080/09537325.2020.1732912.
   Web of Science ®Google Scholar
• Friedman, A. 2019. Data science syllabi measuring its content. Education and Information Technologies 24 (6):3467–81. doi:10.1007/s10639-019-09935-x.
   Web of Science ®Google Scholar
• Furht, B., and F. Villanustre. 2016. Introduction to big data. In Big data technologies and applications, ed. B. Furht and F. Villanustre, 3–11. Cham: Springer International Publishing. doi:10.1007/978-3-319-44550-2_1.
   Google Scholar
• Galetsi, P., and K. Katsaliaki. 2020. Big data analytics in health: An overview and bibliometric study of research activity. Health Information & Libraries Journal 37 (1):5–25. doi:10.1111/hir.12286.
   PubMed Web of Science ®Google Scholar
• Gandomi, A., and M. Haider. 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management 35 (2):137–44. doi:10.1016/j.ijinfomgt.2014.10.007.
   Web of Science ®Google Scholar
• Gao, W., Q. Qiu, C. Yuan, X. Shen, F. Cao, G. Wang, and G. Wang. 2022. Forestry big data: A review and bibliometric analysis. Forests 13 (10):1549. doi:10.3390/f13101549.
   Web of Science ®Google Scholar
• Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. doi:10.1016/j.rse.2017.06.031.
   Web of Science ®Google Scholar
• Gurjit, K., and T. Pradeep. 2019. Handbook of research on big data and the IoT. USA: IGI Global.
   Google Scholar
• Hang, G., Z. Hongru, and R. Zhiwei. 2020. Current situation and countermeasure analysis of big data network public opinion research in china and abroad based on bibliometrics: —taking the study of internet public opinion in universities as an example. In 2020 International Conference on Big Data and Social Sciences (ICBDSS), 207–12. Xi’an, China: IEEE. doi:10.1109/ICBDSS51270.2020.00053.
   Google Scholar
• Hashem, I. A. T., I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. Ullah Khan. 2015. The rise of “big data” on cloud computing: Review and open research issues. Information Systems 47:98–115. doi:10.1016/j.is.2014.07.006.
   Web of Science ®Google Scholar
• Hou, Y., and Q. Wang. 2022. Big data and artificial intelligence application in energy field: A bibliometric analysis. Environmental Science and Pollution Research 30 (6):13960–73. doi:10.1007/s11356-022-24880-y.
   PubMed Web of Science ®Google Scholar
• Inamdar, Z., R. Raut, V. S. Narwane, B. Gardas, B. Narkhede, and M. Sagnak. 2021. A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018. Journal of Enterprise Information Management 34 (1):101–39. doi:10.1108/JEIM-09-2019-0267.
   Web of Science ®Google Scholar
• Jin, Y., and X. Li. 2019. Visualizing the hotspots and emerging trends of multimedia big data through scientometrics. Multimedia Tools and Applications 78 (2):1289–313. doi:10.1007/s11042-018-6172-5.
   Web of Science ®Google Scholar
• Jin, R., H. Yuan, and Q. Chen. 2019. Science mapping approach to assisting the review of construction and demolition waste management research published between 2009 and 2018. Resources Conservation and Recycling 140 (January):175–88. doi:10.1016/j.resconrec.2018.09.029.
   Web of Science ®Google Scholar
• Jun, L., H. HanPing, and R. Liu. 2018. Data restoration based on Gaussian noisy and motion-blurred snapshots in multimedia big data. Multimedia Tools and Applications 77 (8):9959–77. doi:10.1007/s11042-017-4515-2.
   Web of Science ®Google Scholar
• Kalantari, A., A. Kamsin, H. Shukri Kamaruddin, N. Ale Ebrahim, A. Gani, A. Ebrahimi, and S. Shamshirband. 2017. A bibliometric approach to tracking big data research trends. Journal of Big Data 4 (1):30. doi:10.1186/s40537-017-0088-1.
   Google Scholar
• Kamioka, T., and T. Tapanainen. 2014. Biochemical and biophysical characterization of an unexpected bacteriolytic activity of VanX, a member of the vancomycin-resistance vanA gene cluster. The Journal of Biological Chemistry 289 (52):35686–94. doi:10.1074/jbc.M114.590265.
   PubMed Web of Science ®Google Scholar
• Khanra, S., A. Dhir, and M. Mäntymäki. 2020. Big data analytics and enterprises: A bibliometric synthesis of the literature. Enterprise Information Systems 14 (6):737–68. doi:10.1080/17517575.2020.1734241.
   Web of Science ®Google Scholar
• Kholidah, H., H. Yuliatul Hijriah, I. Mawardi, N. Huda, S. Herianingrum, and B. Alkausar. 2022. A bibliometric mapping of peer-to-peer lending research based on economic and business perspective. Heliyon 8 (11):e11512. doi:10.1016/j.heliyon.2022.e11512.
   PubMed Web of Science ®Google Scholar
• Kokol, P., M. Kokol, and S. Zagoranski. 2022. Machine learning on small size samples: A synthetic knowledge synthesis. Science Progress 105 (1):003685042110297. doi:10.1177/00368504211029777.
   Web of Science ®Google Scholar
• Kramer, A. D. I., J. E. Guillory, and J. T. Hancock. 2014. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences 111 (24):8788–90. doi:10.1073/pnas.1320040111.
   PubMed Web of Science ®Google Scholar
• Kyritsi, K. H., V. Zorkadis, E. C. Stavropoulos, and V. S. Verykios. 2019. The pursuit of patterns in educational data mining as a threat to student privacy. Journal of Interactive Media in Education 2019 (1). doi:10.5334/jime.502.
   Web of Science ®Google Scholar
• Layus, P., P. Kah, J. Martikainen, and R. Suoranta. 2015. Big data bibliometric research of welding scientific publications in a 10-Year period. 182–88.
   Google Scholar
• Lazer, D., R. Kennedy, G. King, and A. Vespignani. 2014. The Parable of Google Flu: Traps in Big Data Analysis. Science 343 (6176):1203–05. doi:10.1126/science.1248506.
   PubMed Web of Science ®Google Scholar
• Liang, T.-P., and Y.-H. Liu. 2018. Research landscape of business intelligence and big data analytics: a bibliometrics study. Expert Systems with Applications 111:2–10. doi:10.1016/j.eswa.2018.05.018.
   Web of Science ®Google Scholar
• Liao, H., M. Tang, L. Luo, C. Li, F. Chiclana, and X.-J. Zeng. 2018. A bibliometric analysis and visualization of medical big data research. Sustainability (Switzerland) 10 (2):1. doi:https://doi.org/10.3390/su10010166.
   PubMed Web of Science ®Google Scholar
• Liao, H.T., Z. Wang, and Y. Liu. 2020. Exploring the cross-disciplinary collaboration: A scientometric analysis of social science research related to artificial intelligence and big data application. IOP Conference Series: Materials Science and Engineering 806 (1):012019. doi:10.1088/1757-899X/806/1/012019.
   Google Scholar
• Liu, X., R. Sun, S. Wang, and Y. J. Wu. 2019. The research landscape of big data: A bibliometric analysis. Library Hi Tech 38 (2):367–84. doi:https://doi.org/10.1108/LHT-01-2019-0024.
   Web of Science ®Google Scholar
• Liu, J., X. Zhai, and X. Liao. 2020. Bibliometric Analysis on Cardiovascular Disease Treated by Traditional Chinese Medicines Based on Big Data. International Journal of Parallel, Emergent and Distributed Systems 35 (3):323–39. doi:10.1080/17445760.2019.1606912.
   Web of Science ®Google Scholar
• Lv, Y., Y. Duan, W. Kang, Z. Li, and F. -Y. Wang (2014). Traffic Flow Prediction with Big Data: A Deep Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 1–9. 10.1109/TITS.2014.2345663
   Google Scholar
• Maosen, Y., L. Yang, and L. Jiaqing 2019. Research on information management of big data based on bibliometrics. In 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 1465–69. Chongqing, China: IEEE. doi:10.1109/IMCEC46724.2019.8983947.
   Google Scholar
• McAfee, A., and E. Brynjolfsson. 2012. Big data: The management revolution. Harvard Business Review 90 (10):60–6, 68, 128.
   PubMed Web of Science ®Google Scholar
• Mishra, D., A. Gunasekaran, T. Papadopoulos, and S. J. Childe. 2018. Big data and supply chain management: A review and bibliometric analysis. Annals of Operations Research 270 (1–2):313–36. doi:10.1007/s10479-016-2236-y.
   Web of Science ®Google Scholar
• Mkrttchian, V., I. Krevskiy, A. Bershadsky, T. Glotova, L. Gamidullaeva, and S. Vasin. 2019. Web-based learning and development of university’s electronic informational educational environment. International Journal of Web-Based Learning and Teaching Technologies 14 (1):32–52. doi:10.4018/IJWLTT.2019010103.
   Google Scholar
• Nobre, G. C., and E. Tavares. 2017. Scientific literature analysis on big data and internet of things applications on circular economy: A bibliometric study. Scientometrics 111 (1):463–92. doi:10.1007/s11192-017-2281-6.
   Web of Science ®Google Scholar
• Oravec, C. S., M. Motiwala, K. Reed, T. L. Jones, and P. Klimo Jr. 2019. Big data research in pediatric neurosurgery: content, statistical output, and bibliometric analysis. Pediatric Neurosurgery 54 (2):85–97. doi:10.1159/000495790.
   PubMed Web of Science ®Google Scholar
• Oussous, A., F.Z. Benjelloun, A. Ait Lahcen, and S. Belfkih. 2018. Big data technologies: A survey. Journal of King Saud University - Computer and Information Sciences 30 (4):431–48. doi:10.1016/j.jksuci.2017.06.001.
   Web of Science ®Google Scholar
• Owayid, A. M., and L. Uden. 2014. The usage of google apps services in higher education. Communications in Computer and Information Science 446 CCIS 95–104. doi:10.1007/978-3-319-10671-7_9.
   Google Scholar
• Parlina, A., K. Ramli, and H. Murfi. 2020. Theme mapping and bibliometrics analysis of one decade of big data research in the scopus database. Information 11 (2):69. doi:10.3390/info11020069.
   Web of Science ®Google Scholar
• Payne, M. E., L. B. Ngo, and A. W. Apon. 2013. Academic publishing as a social media paradigm. 9–12. doi:10.1109/BigData.2013.6691763.
   Google Scholar
• Philip Chen, C. L., and C.Y. Zhang. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences 275 (August):314–47. doi:10.1016/j.ins.2014.01.015.
   Web of Science ®Google Scholar
• Piety, P. J., D. T. Hickey, and M. J. Bishop. 2014. Educational data sciences - framing emergent practices for analytics of learning, organizations, and systems. 193–202. doi:10.1145/2567574.2567582.
   Google Scholar
• Rajeswari, S., and K. Praveena. 2021. Analysis of ‘big data’ research output in IEEExplore: A bibliometric study. The Library 2021:1–12.
   Google Scholar
• Rath, M. 2022. Intelligent information system for academic institutions: using big data analytic approach. Research Anthology on Big Data Analytics, Architectures, and Applications 2:788–806. doi:10.4018/978-1-6684-3662-2.ch036.
   Google Scholar
• Reddy, S. S. K., and S. Chao. 2020. Academic collaborations with industry: Lessons for the future. Journal of Investigative Medicine 68 (8):1305–08. doi:10.1136/jim-2020-001636.
   PubMed Web of Science ®Google Scholar
• Resor, E., S. Legovini, S. Milusheva, R. Marty, G. Bedoya, A. Williams, and F. Chen. 2021. Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning. Plos One 16 (2):e0244317. 2 February. doi:https://doi.org/10.1371/journal.pone.0244317.
   PubMed Web of Science ®Google Scholar
• Rialti, R., G. Marzi, C. Ciappei, and D. Busso. 2019. Big data and dynamic capabilities: A Bibliometric analysis and systematic literature review. Management Decision 57 (8):2052–68. doi:10.1108/MD-07-2018-0821.
   Web of Science ®Google Scholar
• Riazul Islam, S. M., D. Kwak, M. Humaun Kabir, M. Hossain, and K.S. Kwak. 2015. The Internet of Things for Health Care: A Comprehensive Survey. IEEE Access 3:678–708. doi:10.1109/ACCESS.2015.2437951.
   Web of Science ®Google Scholar
• Rivai, F. A., N. J. Navimipour, and S. Yalcın. 2023. Multimedia big data computing mechanisms: A bibliometric analysis. Multimedia Tools and Applications 82 (2):2765–81. doi:10.1007/s11042-022-12988-9.
   Web of Science ®Google Scholar
• Rozas, J., J. C. Sanchez-DelBarrio, X. Messeguer, and R. Rozas. 2003. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 19 (18):2496–97. doi:10.1093/bioinformatics/btg359.
   PubMed Web of Science ®Google Scholar
• “RStudio Release Notes”. 2023. https://www.rstudio.com/products/rstudio/release-notes/.
   Google Scholar
• Ruixian, Y. 2013. Bibliometrical analysis on the big data research in China. Journal of Digital Information Management 11 (6):383–90.
   Google Scholar
• Sahoo, S. 2022. Big data analytics in manufacturing: A bibliometric analysis of research in the field of business management. International Journal of Production Research 60 (22):6793–821. doi:10.1080/00207543.2021.1919333.
   Web of Science ®Google Scholar
• Schoenthaler, M., M. Boeker, and P. Horki. 2019. How to compete with google and co.: Big data and artificial intelligence in stones. Current Opinion in Urology 29 (2):135–42. doi:10.1097/MOU.0000000000000578.
   PubMed Web of Science ®Google Scholar
• Shukla, A. K., P. K. Muhuri, and A. Abraham. 2020. A bibliometric analysis and cutting-edge overview on fuzzy techniques in big data. Engineering Applications of Artificial Intelligence 92 (June):103625. doi:10.1016/j.engappai.2020.103625.
   Web of Science ®Google Scholar
• Singh, V. K., S. Kumar Banshal, K. Singhal, and A. Uddin. 2015. Scientometric mapping of research on ‘big data.’. Scientometrics 105 (2):727–41. doi:10.1007/s11192-015-1729-9.
   Web of Science ®Google Scholar
• Skoric, M. M. 2014. The implications of big data for developing and transitional economies: Extending the triple helix? Scientometrics 99 (1):175–86. doi:10.1007/s11192-013-1106-5.
   Web of Science ®Google Scholar
• Smith, J. R. 2013. Riding the multimedia big data wave. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1–2. Dublin Ireland: ACM. doi:10.1145/2484028.2494492.
   Google Scholar
• Soleimani-Roozbahani, F., A. Rajabzadeh Ghatari, and R. Radfar. 2019. Knowledge discovery from a more than a decade studies on healthcare big data systems: A scientometrics study. Journal of Big Data 6 (1):8. doi:10.1186/s40537-018-0167-y.
   Google Scholar
• Sun, B. -Y., P. Wang, L. -T. Tang, H. -Z. Feng, and W. -Y. Cai. 2020. Big data analysis of proficiency testing in fields of chemistry material test and analysis based on bibliometrics. Yejin Fenxi/Metallurgical Analysis 40 (5):1–8. doi:10.13228/j.boyuan.issn1000-7571.010965.
   Google Scholar
• Tang, Z., C. Li, B. Kang, G. Gao, C. Li, and Z. Zhang. 2017. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic acids research 45 (W1):W98–102. doi:10.1093/nar/gkx247.
   PubMed Web of Science ®Google Scholar
• “VOSviewer - Visualizing Scientific Landscapes”. 2023. Vosviewer. https://www.vosviewer.com//.
   Google Scholar
• Wang, Z., and H. -T. Liao. 2020. Towards the eco-design of artificial intelligence and big data applications: A bibliometric analysis of related research. IOP Conference Series: Materials Science and Engineering 806 (1):012039. doi:https://doi.org/10.1088/1757-899X/806/1/012039.
   Google Scholar
• Wang, H., J. Shi, S. Shi, B. Rongqiang, X. Zhang, and H. Yuanhui. 2022. Bibliometric analysis on the progress of chronic heart failure. Current Problems in Cardiology 47 (9):101213. doi:10.1016/j.cpcardiol.2022.101213.
   PubMed Web of Science ®Google Scholar
• Wang, Z., K. Simon, A. Makai, and M. Jaromi. 2022. A bibliometric analysis of self-efficacy in low back pain from 1980 to 2021. Pain Practice 13201. December. doi:10.1111/papr.13201.
   Web of Science ®Google Scholar
• Wang, K., Z. Sun, M. Cai, L. Liu, H. Wu, and Z. Peng. 2022. Impacts of urban blue-green space on residents’ health: A bibliometric review. International Journal of Environmental Research and Public Health 19 (23):16192. doi:https://doi.org/10.3390/ijerph192316192.
   PubMed Web of Science ®Google Scholar
• Wilhelm, M., J. Schlegl, H. Hahne, A. M. Gholami, M. Lieberenz, M. M. Savitski, E. Ziegler, L. Butzmann, S. Gessulat, H. Marx, et al. 2014. Mass-spectrometry-based draft of the human proteome. Nature 509 (7502):582–87. doi:10.1038/nature13319.
   PubMed Web of Science ®Google Scholar
• Xindong, W., X. Zhu, W. Gong-Qing, and W. Ding. 2014. Data mining with big data. IEEE Transactions on Knowledge and Data Engineering 26 (1):97–107. doi:10.1109/TKDE.2013.109.
   Web of Science ®Google Scholar
• Xu, L. D., W. He, and S. Li. 2014. Internet of Things in Industries: A Survey. IEEE Transactions on Industrial Informatics 10 (4):2233–43. doi:10.1109/TII.2014.2300753.
   Web of Science ®Google Scholar
• Yang, C.-L., C.-Y. Huang, Y.-S. Kao, and Y.-L. Tasi. 2017. Disaster recovery site evaluations and selections for information systems of academic big data. Eurasia Journal of Mathematics, Science and Technology Education 13 (8):4553–89. doi:10.12973/eurasia.2017.00951a.
   Web of Science ®Google Scholar
• Zhang, Y., Y. Huang, A. L. Porter, G. Zhang, and L. Jie 2017. Discovering interactions in big data research: a learning-enhanced bibliometric study. In 2017 Portland International Conference on Management of Engineering and Technology (PICMET), 1–12. Portland, OR: IEEE. doi:10.23919/PICMET.2017.8125292.
   Google Scholar
• Zhang, Y., Y. Huang, A. L. Porter, G. Zhang, and J. Lu. 2019. Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study. Technological Forecasting and Social Change 146 (September):795–807. doi:10.1016/j.techfore.2018.06.007.
   Web of Science ®Google Scholar
• Zhang, S., D. Liu, J. Wang, and M. Lv. 2022. Big data analysis of endovascular treatment of intracranial aneurysms: A bibliometric analysis of the top 100 most cited articles. Arquivos de neuro-psiquiatria 80 (12):1189–95. doi:10.1055/s-0042-1758650.
   PubMed Web of Science ®Google Scholar
• Zhang, X., Y. Yanni, and N. Zhang. 2021. Sustainable supply chain management under big data: A bibliometric analysis. Journal of Enterprise Information Management 34 (1):427–45. doi:10.1108/JEIM-12-2019-0381.
   Web of Science ®Google Scholar
• Zhou, Y., B. Zhou, L. Pache, M. Chang, A. H. Khodabakhshi, O. Tanaseichuk, C. Benner, and S. K. Chanda. 2019. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications 10 (1):1523. doi:10.1038/s41467-019-09234-6.
   PubMed Web of Science ®Google Scholar