{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T23:45:04Z","timestamp":1776469504817,"version":"3.51.2"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006359","name":"Blekinge Institute of Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006359","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper a program and methodology for bibliometric mining of research trends and directions is presented. The method is applied to the research area Big Data for the time period 2012 to 2022, using the Scopus database. It turns out that the 10 most important research directions in Big Data are Machine learning, Deep learning and neural networks, Internet of things, Data mining, Cloud computing, Artificial intelligence, Healthcare, Security and privacy, Review, and Manufacturing. The role of Big Data research in different fields of science and technology is also analysed. For four geographic regions (North America, European Union, China, and The Rest of the World) different activity levels in Big Data during different parts of the time period are analysed. North America was the most active region during the first part of the time period. During the last years China is the most active region. The citation scores for documents from different regions and from different research directions within Big Data are also compared. North America has the highest average citation score among the geographic regions and the research direction Review has the highest average citation score among the research directions. The program and methodology for bibliometric mining developed in this study can be used also for other large research areas. Now that the program and methodology have been developed, it is expected that one could perform a similar study in some other research area in a couple of days.<\/jats:p>","DOI":"10.1186\/s40537-023-00793-6","type":"journal-article","created":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T17:01:47Z","timestamp":1688230907000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Bibliometric mining of research directions and trends for big data"],"prefix":"10.1186","volume":"10","author":[{"given":"Lars","family":"Lundberg","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,1]]},"reference":[{"key":"793_CR1","unstructured":"Lohr S. (1 February 2013), \u201cThe Origins of \u2018Big Data\u2019: An Etymological Detective Story\u201d. The New York Times. Archived from the original on 6 March 2016. https:\/\/archive.nytimes.com\/bits.blogs.nytimes.com\/2013\/02\/01\/the-origins-of-big-data-an-etymological-detective-story\/, Retrieved 26 April 2023."},{"key":"793_CR2","unstructured":"Marr B. \u201cHow much data do we create every day? The mind-blowing stats everyone should read,\u201c https:\/\/www.forbes.com\/sites\/bernardmarr\/2018\/05\/21\/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read\/?sh=661e274e60ba, 2018."},{"key":"793_CR3","doi-asserted-by":"publisher","unstructured":"Lundberg L, Grahn H. \u201cResearch Trends, Enabling Technologies and Application Areas for Big Data,\u201d Algorithms, vol.\u00a015, no. 8, p.\u00a0280, 2022, DOI: https:\/\/doi.org\/10.3390\/a15080280.","DOI":"10.3390\/a15080280"},{"key":"793_CR4","doi-asserted-by":"publisher","unstructured":"Speretta M, Gauch S, Lakkaraju P. \u201cUsing CiteSeer to analyze trends in the ACM\u2019s computing classification system,\u201c in 2010,. DOI: https:\/\/doi.org\/10.1109\/HSI.2010.5514510.","DOI":"10.1109\/HSI.2010.5514510"},{"key":"793_CR5","doi-asserted-by":"publisher","unstructured":"Dong Y. \u201cNLP-Based Detection of Mathematics subject classification,\u201d In: Davenport J, Kauers M, Labahn G, Urban J, editors Mathematical Software \u2013 ICMS 2018. ICMS 2018. Lecture notes in Computer Science(), vol 10931. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-96418-8_18.","DOI":"10.1007\/978-3-319-96418-8_18"},{"key":"793_CR6","doi-asserted-by":"publisher","unstructured":"Wang C, Dai J, Xu L. \u201cBig data and data mining in education: A bibliometrics study from 2010 to 2022,\u201c 7th International Conference on Cloud Computing and Big Data Analytics (2022), DOI: https:\/\/doi.org\/10.1109\/ICCCBDA55098.2022.9778874.","DOI":"10.1109\/ICCCBDA55098.2022.9778874"},{"issue":"5","key":"793_CR7","doi-asserted-by":"publisher","first-page":"4659","DOI":"10.3233\/JIFS-179016","volume":"36","author":"V Gupta","year":"2019","unstructured":"Gupta V, et al. A quantitative and text-based characterization of big data research. J Intell Fuzzy Syst. 2019;36(5):4659\u201375.","journal-title":"J Intell Fuzzy Syst"},{"key":"793_CR8","doi-asserted-by":"crossref","unstructured":"Wang W, Lu C. \u201cVisualization analysis of big data research based on Citespace,\u201c Soft Comput (Berlin Germany), vol. 24, (11), pp. 8173\u201386, 2019;2020.","DOI":"10.1007\/s00500-019-04384-7"},{"key":"793_CR9","doi-asserted-by":"crossref","unstructured":"Rawat KS, Sood SK. \u201cEmerging trends and global scope of big data analytics: a scientometric analysis,\u201c Qual Quant, vol. 55, (4), pp. 1371\u201396, 2020;2021.","DOI":"10.1007\/s11135-020-01061-y"},{"issue":"3","key":"793_CR10","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1007\/s11192-020-03371-2","volume":"122","author":"DR Raban","year":"2020","unstructured":"Raban DR, Gordon A. The evolution of data science and big data research: a bibliometric analysis. Scientometrics. 2020;122(3):1563\u201381.","journal-title":"Scientometrics"},{"issue":"3","key":"793_CR11","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1177\/0165551518789880","volume":"45","author":"D Gupta","year":"2019","unstructured":"Gupta D, Rani R. A study of big data evolution and research challenges. J Inform Sci. 2019;45(3):322\u201340.","journal-title":"J Inform Sci"},{"issue":"2","key":"793_CR12","first-page":"69","volume":"11","author":"A Parlina","year":"2020","unstructured":"Parlina A, Ramli K, Murfi H. Theme mapping and bibliometrics analysis of one decade of big data research in the scopus database. Inform (Basel). 2020;11(2):69.","journal-title":"Inform (Basel)"},{"issue":"1\u20132","key":"793_CR13","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s42488-019-00001-2","volume":"1","author":"Z Xu","year":"2019","unstructured":"Xu Z, Yu D. A Bibliometrics analysis on big data research (2009\u20132018). J Data Inform Manage. 2019;1(1\u20132):3\u201315.","journal-title":"J Data Inform Manage"},{"issue":"1","key":"793_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-017-0088-1","volume":"4","author":"A Kalantari","year":"2017","unstructured":"Kalantari A, et al. A bibliometric approach to tracking big data research trends. J Big Data. 2017;4(1):1\u201318.","journal-title":"J Big Data"},{"key":"793_CR15","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.techfore.2018.06.007","volume":"146","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, et al. Discovering and forecasting interactions in big data research: a learning-enhanced bibliometric study. Technological Forecast Social Change. 2019;146:795\u2013807.","journal-title":"Technological Forecast Social Change"},{"key":"793_CR16","doi-asserted-by":"crossref","unstructured":"Lu LYY, Liu JS. \u201cThe major research themes of big data literature: From 2001 to 2016,\u201c in 2016 IEEE International Conference on Computer and Information Technology.","DOI":"10.1109\/CIT.2016.46"},{"issue":"Part 2","key":"793_CR17","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.csi.2017.01.004","volume":"54","author":"J Akoka","year":"2017","unstructured":"Akoka J, Comyn-Wattiau I, Laoufi N. Research on Big Data \u2013 A systematic mapping study. Comput Stand Interfaces. 2017;54(Part 2):105\u201315.","journal-title":"Comput Stand Interfaces"},{"issue":"2","key":"793_CR18","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1108\/LHT-01-2019-0024","volume":"38","author":"X Liu","year":"2020","unstructured":"Liu X, et al. The research landscape of big data: a bibliometric analysis. Libr Hi Tech. 2020;38(2):367\u201384.","journal-title":"Libr Hi Tech"},{"key":"793_CR19","first-page":"193","volume-title":"Lecture notes in Computer Science (including Subseries lecture notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","author":"E Herrera-Viedma","year":"2016","unstructured":"Herrera-Viedma E, Martinez MA, Herrera M. \u201cBibliometric tools for discovering information in database,\u201c Lecture notes in Computer Science (including Subseries lecture notes in Artificial Intelligence and Lecture Notes in Bioinformatics), H. Fujita Eds. Cham: Springer International Publishing, 2016, 193\u2013203."},{"issue":"5","key":"793_CR20","first-page":"1275","volume":"48","author":"M Guti\u00e9rrez-Salcedo","year":"2018","unstructured":"Guti\u00e9rrez-Salcedo M, et al. Some bibliometric procedures for analyzing and evaluating research fields. Appl Intell (Dordrecht Netherlands). 2018;48(5):1275\u201387.","journal-title":"Appl Intell (Dordrecht Netherlands)"},{"issue":"4","key":"793_CR21","doi-asserted-by":"publisher","first-page":"e0231735","DOI":"10.1371\/journal.pone.0231735","volume":"15","author":"A Jappe","year":"2020","unstructured":"Jappe A. Professional standards in bibliometric research evaluation? A meta-evaluation of european assessment practice 2005\u20132019. PLoS ONE. 2020;15(4):e0231735.","journal-title":"PLoS ONE"},{"issue":"2","key":"793_CR22","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1007\/s11192-017-2506-8","volume":"113","author":"JM Campanario","year":"2017","unstructured":"Campanario JM. JIF-Plots: using plots of citations versus citable items as a tool to study journals and subject categories and discover new scientometric relationships. Scientometrics. 2017;113(2):1141\u201354.","journal-title":"Scientometrics"},{"issue":"4","key":"793_CR23","doi-asserted-by":"publisher","first-page":"221","DOI":"10.3103\/S0147688220040036","volume":"47","author":"NA Mazov","year":"2020","unstructured":"Mazov NA, Gureev VN, Glinskikh VN. The methodological basis of defining Research Trends and Fronts. Sci Tech Inform Process. 2020;47(4):221\u201331.","journal-title":"Sci Tech Inform Process"},{"key":"793_CR24","unstructured":"Analytics C. \u201cResearch Fronts 2021,\u201d https:\/\/discover.clarivate.com\/ResearchFronts2021_EN, 2022. Visited April 29, 2023."},{"key":"793_CR25","doi-asserted-by":"crossref","unstructured":"van Eck NJ, Waltman L. \u201cVisualizing bibliometric networks,\u201c Measuring Scholarly Impact, Springer International Publishing, 2014, 285\u2013320.","DOI":"10.1007\/978-3-319-10377-8_13"},{"key":"793_CR26","doi-asserted-by":"crossref","unstructured":"Amjad T et al. \u201cCitation burst prediction in a bibliometric network,\u201c Scientometrics, vol.\u00a0127, (5), pp.\u00a02773\u20132790, 2022.","DOI":"10.1007\/s11192-022-04344-3"},{"issue":"8","key":"793_CR27","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.1002\/asi.23814","volume":"68","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, et al. Scientific evolutionary pathways: identifying and visualizing relationships for scientific topics. J Association Inform Sci Technol. 2017;68(8):1925\u201339.","journal-title":"J Association Inform Sci Technol"},{"issue":"3","key":"793_CR28","doi-asserted-by":"publisher","first-page":"e18029","DOI":"10.1371\/journal.pone.0018029","volume":"6","author":"KW Boyack","year":"2011","unstructured":"Boyack KW, et al. Clustering more than two million biomedical publications: comparing the accuracies of nine text-based similarity approaches. PLoS ONE. 2011;6(3):e18029\u20139.","journal-title":"PLoS ONE"},{"key":"793_CR29","doi-asserted-by":"crossref","unstructured":"Guzm\u00e1n S\u00e1nchez MV, \u201cCHEN, CHAOMEI, CiteSpace: A Practical Guide for Mapping Scientific Literature., Hauppauge NY, Nova Science. 2016, 169 pp. ISBN: 978-1-53610-280-2: eBook: 978-1-53610- 295-6 [CiteSpace: una gu\u00eda pr\u00e1ctica para el mapeo de la literatura cient\u00edfica],\u201c Investigaci\u00f3n Bibliotecol\u00f3gica, vol.\u00a031, (nesp1), pp.\u00a0293\u2013295, 2018;2017.","DOI":"10.22201\/iibi.24488321xe.2017.nesp1.57894"},{"issue":"2","key":"793_CR30","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1080\/07317131.2018.1425352","volume":"35","author":"D Wong","year":"2018","unstructured":"Wong D. VOSviewer. Tech Serv Q. 2018;35(2):219\u201320.","journal-title":"Tech Serv Q"},{"key":"793_CR31","unstructured":"van Eck NJ, Waltman L. \u201cText mining and visualization using VOSviewer,\u201c https:\/\/arxiv.org\/abs\/1109.2058, 2011."},{"issue":"2","key":"793_CR32","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1080\/09737766.2021.1960220","volume":"15","author":"B Markscheffel","year":"2021","unstructured":"Markscheffel B, Schr\u00f6ter F. Comparison of two science mapping tools based on software technical evaluation and bibliometric case studies. Collnet J Scientometrics Inform Manage. 2021;15(2):365\u201396.","journal-title":"Collnet J Scientometrics Inform Manage"},{"key":"793_CR33","doi-asserted-by":"publisher","first-page":"100263","DOI":"10.1016\/j.softx.2019.100263","volume":"10","author":"ME Rose","year":"2019","unstructured":"Rose ME, Kitchin JR. Pybliometrics: Scriptable bibliometrics using a Python interface to Scopus. Softwarex. 2019;10:100263.","journal-title":"Softwarex"},{"key":"793_CR34","doi-asserted-by":"crossref","unstructured":"Zhu J et al. \u201cMeasuring recent research performance for Chinese universities using bibliometric methods,\u201c Scientometrics, vol.\u00a0101, (1), pp.\u00a0429\u2013443, 2014.","DOI":"10.1007\/s11192-014-1389-1"},{"issue":"10","key":"793_CR35","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1002\/asi.24184","volume":"70","author":"F Shu","year":"2019","unstructured":"Shu F, Julien C, Larivi\u00e8re V. Does the web of science accurately represent chinese scientific performance? J Association Inform Sci Technol. 2019;70(10):1138\u201352.","journal-title":"J Association Inform Sci Technol"},{"key":"793_CR36","doi-asserted-by":"publisher","first-page":"100244","DOI":"10.1016\/j.bdr.2021.100244","volume":"26","author":"L Lundberg","year":"2021","unstructured":"Lundberg L, et al. Editorial to the special issue on Big Data in Industrial and Commercial Applications. Big Data Research. 2021;26:100244.","journal-title":"Big Data Research"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00793-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-023-00793-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00793-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T13:39:47Z","timestamp":1729690787000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-023-00793-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,1]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["793"],"URL":"https:\/\/doi.org\/10.1186\/s40537-023-00793-6","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,1]]},"assertion":[{"value":"30 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"There is only one author (Lars Lundberg), and he gives his consent for publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"112"}}