Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22125
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dc.contributor.authorWang, X-
dc.contributor.authorChang, Y-
dc.contributor.authorXu, Z-
dc.contributor.authorWang, Z-
dc.contributor.authorKadirkamanathan, V-
dc.date.accessioned2021-01-20T18:14:54Z-
dc.date.available2021-01-20T18:14:54Z-
dc.date.issued2020-12-29-
dc.identifierORCID iD: Zidong Wang https://orcid.org/0000-0002-9576-7401-
dc.identifier.citationWang, X. et al. (2020) '50 Years of international journal of systems science: a review of the past and trends for the future', International Journal of Systems Science, 52 (8), pp. 1515 - 1538. doi: 10.1080/00207721.2020.1862937.en_US
dc.identifier.issn0020-7721-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/22125-
dc.description.abstractInternational Journal of Systems Science (IJSS) is an international journal in the fields of Automation & Control Systems, Computer Science, and Operations Research & Management Science. This paper is a celebration of the 50th anniversary of IJSS. A Web of Science count of 7528 documents were derived from 1970 to 2019 as data sources for the bibliometric analysis. First, the fundamental characteristics of the documents were identified from the bibliometric indicators, and features of keywords were revealed over the half century. A timeline and occurrence analyses were conducted via the software tools, through which popular keywords were visualised in different time periods. These were then grouped into four categories by a hierarchical clustering method, and emerging keywords were selected using burst detection analysis. This was followed by the Autoregressive Integrated Moving Average modelling to predict the future research trends in the four clusters as evidenced in the historical data. Documents were discussed in the recent 20 years towards guiding scholars in three aspects, i.e. problems addressed, popular methodologies, and impact in the relevant fields. Finally, a summary of the main findings was given in the conclusions.-
dc.description.sponsorshipNational Natural Science Foundation of China [grant number No. 71571123,No. 71771155].en_US
dc.format.extent1515 - 1538-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherRoutledge (Taylor & Francis Group)-
dc.rightsCopyright © 2020 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Systems Science on 29 Dec 2020, available online: https://www.tandfonline.com/doi/full/10.1080/00207721.2020.1862937, made available on this repository under a Creative Commons CC BY-NC attribution licence (https://creativecommons.org/licenses/by-nc/4.0/).-
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/-
dc.subjectbibliometric analysisen_US
dc.subjecthierarchical clusteringen_US
dc.subjectautoregressive integrated moving average modelen_US
dc.title50 Years of international journal of systems science: a review of the past and trends for the futureen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1080/00207721.2020.1862937-
dc.relation.isPartOfInternational Journal of Systems Science-
pubs.issue8-
pubs.publication-statusPublished-
pubs.volume52-
dc.identifier.eissn1464-5319-
dc.rights.holderInforma UK Limited, trading as Taylor & Francis Group-
Appears in Collections:Dept of Computer Science Research Papers

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