VISPubComPAS: a comparative analytical system for visualization publication data

For an unfamiliar field, researchers who are looking for interdisciplinary collaboration or students who are going to start their research career often need to look for top research affiliations and domain experts according to the publication of top conferences or journals in this field. Further com...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of visualization 2019-10, Vol.22 (5), p.941-953
Hauptverfasser: Wang, Yang, Yu, Minzhu, Shan, Guihua, Shen, Han-Wei, Lu, Zhonghua
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 953
container_issue 5
container_start_page 941
container_title Journal of visualization
container_volume 22
creator Wang, Yang
Yu, Minzhu
Shan, Guihua
Shen, Han-Wei
Lu, Zhonghua
description For an unfamiliar field, researchers who are looking for interdisciplinary collaboration or students who are going to start their research career often need to look for top research affiliations and domain experts according to the publication of top conferences or journals in this field. Further comparative analysis of affiliations or experts with similar achievements is also needed in order to find suitable collaborators or supervisors. In this work, we provide comprehensive visual analysis of research affiliations and domain experts based on papers accepted by the IEEE VIS from 1990 to 2018. First, we extract multi-word keywords from title and abstract automatically and then extract topics using LDA model based on these keywords. Second, we extract relationship between authors and affiliations based on co-author analysis. Third, we design and implement VISPubComPAS, a requirement-driven analysis system to (1) help users discover top affiliations and experts of required keywords; (2) visualize the relationships and statistics of these affiliations and experts; (3) compare two selected affiliations or experts of interest in detail by visualization. Finally, we conduct use cases and user reviews to demonstrate the effectiveness of VISPubComPAS. Graphic abstract
doi_str_mv 10.1007/s12650-019-00585-2
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2307440145</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2307440145</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-608d8808500b24fff4c247b49c1b6dc58ca1b5386b70e5e8ebe0e3e4131869483</originalsourceid><addsrcrecordid>eNp9kF1LwzAUhoMoOKd_wKuA19GTJmlT78bwYzBwMPU2JFkqHe1Sk3Ywf71xFbzz6rwcnvdweBC6pnBLAYq7SLNcAAFaEgAhBclO0ITKQhBZFuI0ZcYZkWlxji5i3AJklBd0glbvi_VqMHPfrmbre6yx9W2ng-7rvcN6p5tDX1vd4HiIvWtx5QPe13HQTf2VGL_D3WCaRBzzRvf6Ep1Vuonu6ndO0dvjw-v8mSxfnhbz2ZJYRsue5CA3UoIUACbjVVVxm_HC8NJSk2-skFZTI5jMTQFOOOmMA8ccp4zKvOSSTdHNeLcL_nNwsVdbP4T0cFQZg4JzoFwkKhspG3yMwVWqC3Wrw0FRUD_m1GhOJXPqaC61p4iNpZjg3YcLf6f_aX0D94xwxQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2307440145</pqid></control><display><type>article</type><title>VISPubComPAS: a comparative analytical system for visualization publication data</title><source>SpringerLink Journals - AutoHoldings</source><creator>Wang, Yang ; Yu, Minzhu ; Shan, Guihua ; Shen, Han-Wei ; Lu, Zhonghua</creator><creatorcontrib>Wang, Yang ; Yu, Minzhu ; Shan, Guihua ; Shen, Han-Wei ; Lu, Zhonghua</creatorcontrib><description>For an unfamiliar field, researchers who are looking for interdisciplinary collaboration or students who are going to start their research career often need to look for top research affiliations and domain experts according to the publication of top conferences or journals in this field. Further comparative analysis of affiliations or experts with similar achievements is also needed in order to find suitable collaborators or supervisors. In this work, we provide comprehensive visual analysis of research affiliations and domain experts based on papers accepted by the IEEE VIS from 1990 to 2018. First, we extract multi-word keywords from title and abstract automatically and then extract topics using LDA model based on these keywords. Second, we extract relationship between authors and affiliations based on co-author analysis. Third, we design and implement VISPubComPAS, a requirement-driven analysis system to (1) help users discover top affiliations and experts of required keywords; (2) visualize the relationships and statistics of these affiliations and experts; (3) compare two selected affiliations or experts of interest in detail by visualization. Finally, we conduct use cases and user reviews to demonstrate the effectiveness of VISPubComPAS. Graphic abstract</description><identifier>ISSN: 1343-8875</identifier><identifier>EISSN: 1875-8975</identifier><identifier>DOI: 10.1007/s12650-019-00585-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Classical and Continuum Physics ; Computer Imaging ; Engineering ; Engineering Fluid Dynamics ; Engineering Thermodynamics ; Heat and Mass Transfer ; Keywords ; Pattern Recognition and Graphics ; Regular Paper ; Subject specialists ; Supervisors ; Vision ; Visualization</subject><ispartof>Journal of visualization, 2019-10, Vol.22 (5), p.941-953</ispartof><rights>The Visualization Society of Japan 2019</rights><rights>Copyright Springer Nature B.V. 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-608d8808500b24fff4c247b49c1b6dc58ca1b5386b70e5e8ebe0e3e4131869483</citedby><cites>FETCH-LOGICAL-c319t-608d8808500b24fff4c247b49c1b6dc58ca1b5386b70e5e8ebe0e3e4131869483</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12650-019-00585-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12650-019-00585-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Wang, Yang</creatorcontrib><creatorcontrib>Yu, Minzhu</creatorcontrib><creatorcontrib>Shan, Guihua</creatorcontrib><creatorcontrib>Shen, Han-Wei</creatorcontrib><creatorcontrib>Lu, Zhonghua</creatorcontrib><title>VISPubComPAS: a comparative analytical system for visualization publication data</title><title>Journal of visualization</title><addtitle>J Vis</addtitle><description>For an unfamiliar field, researchers who are looking for interdisciplinary collaboration or students who are going to start their research career often need to look for top research affiliations and domain experts according to the publication of top conferences or journals in this field. Further comparative analysis of affiliations or experts with similar achievements is also needed in order to find suitable collaborators or supervisors. In this work, we provide comprehensive visual analysis of research affiliations and domain experts based on papers accepted by the IEEE VIS from 1990 to 2018. First, we extract multi-word keywords from title and abstract automatically and then extract topics using LDA model based on these keywords. Second, we extract relationship between authors and affiliations based on co-author analysis. Third, we design and implement VISPubComPAS, a requirement-driven analysis system to (1) help users discover top affiliations and experts of required keywords; (2) visualize the relationships and statistics of these affiliations and experts; (3) compare two selected affiliations or experts of interest in detail by visualization. Finally, we conduct use cases and user reviews to demonstrate the effectiveness of VISPubComPAS. Graphic abstract</description><subject>Classical and Continuum Physics</subject><subject>Computer Imaging</subject><subject>Engineering</subject><subject>Engineering Fluid Dynamics</subject><subject>Engineering Thermodynamics</subject><subject>Heat and Mass Transfer</subject><subject>Keywords</subject><subject>Pattern Recognition and Graphics</subject><subject>Regular Paper</subject><subject>Subject specialists</subject><subject>Supervisors</subject><subject>Vision</subject><subject>Visualization</subject><issn>1343-8875</issn><issn>1875-8975</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kF1LwzAUhoMoOKd_wKuA19GTJmlT78bwYzBwMPU2JFkqHe1Sk3Ywf71xFbzz6rwcnvdweBC6pnBLAYq7SLNcAAFaEgAhBclO0ITKQhBZFuI0ZcYZkWlxji5i3AJklBd0glbvi_VqMHPfrmbre6yx9W2ng-7rvcN6p5tDX1vd4HiIvWtx5QPe13HQTf2VGL_D3WCaRBzzRvf6Ep1Vuonu6ndO0dvjw-v8mSxfnhbz2ZJYRsue5CA3UoIUACbjVVVxm_HC8NJSk2-skFZTI5jMTQFOOOmMA8ccp4zKvOSSTdHNeLcL_nNwsVdbP4T0cFQZg4JzoFwkKhspG3yMwVWqC3Wrw0FRUD_m1GhOJXPqaC61p4iNpZjg3YcLf6f_aX0D94xwxQ</recordid><startdate>20191001</startdate><enddate>20191001</enddate><creator>Wang, Yang</creator><creator>Yu, Minzhu</creator><creator>Shan, Guihua</creator><creator>Shen, Han-Wei</creator><creator>Lu, Zhonghua</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20191001</creationdate><title>VISPubComPAS: a comparative analytical system for visualization publication data</title><author>Wang, Yang ; Yu, Minzhu ; Shan, Guihua ; Shen, Han-Wei ; Lu, Zhonghua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-608d8808500b24fff4c247b49c1b6dc58ca1b5386b70e5e8ebe0e3e4131869483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Classical and Continuum Physics</topic><topic>Computer Imaging</topic><topic>Engineering</topic><topic>Engineering Fluid Dynamics</topic><topic>Engineering Thermodynamics</topic><topic>Heat and Mass Transfer</topic><topic>Keywords</topic><topic>Pattern Recognition and Graphics</topic><topic>Regular Paper</topic><topic>Subject specialists</topic><topic>Supervisors</topic><topic>Vision</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yang</creatorcontrib><creatorcontrib>Yu, Minzhu</creatorcontrib><creatorcontrib>Shan, Guihua</creatorcontrib><creatorcontrib>Shen, Han-Wei</creatorcontrib><creatorcontrib>Lu, Zhonghua</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of visualization</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Yang</au><au>Yu, Minzhu</au><au>Shan, Guihua</au><au>Shen, Han-Wei</au><au>Lu, Zhonghua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>VISPubComPAS: a comparative analytical system for visualization publication data</atitle><jtitle>Journal of visualization</jtitle><stitle>J Vis</stitle><date>2019-10-01</date><risdate>2019</risdate><volume>22</volume><issue>5</issue><spage>941</spage><epage>953</epage><pages>941-953</pages><issn>1343-8875</issn><eissn>1875-8975</eissn><abstract>For an unfamiliar field, researchers who are looking for interdisciplinary collaboration or students who are going to start their research career often need to look for top research affiliations and domain experts according to the publication of top conferences or journals in this field. Further comparative analysis of affiliations or experts with similar achievements is also needed in order to find suitable collaborators or supervisors. In this work, we provide comprehensive visual analysis of research affiliations and domain experts based on papers accepted by the IEEE VIS from 1990 to 2018. First, we extract multi-word keywords from title and abstract automatically and then extract topics using LDA model based on these keywords. Second, we extract relationship between authors and affiliations based on co-author analysis. Third, we design and implement VISPubComPAS, a requirement-driven analysis system to (1) help users discover top affiliations and experts of required keywords; (2) visualize the relationships and statistics of these affiliations and experts; (3) compare two selected affiliations or experts of interest in detail by visualization. Finally, we conduct use cases and user reviews to demonstrate the effectiveness of VISPubComPAS. Graphic abstract</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12650-019-00585-2</doi><tpages>13</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1343-8875
ispartof Journal of visualization, 2019-10, Vol.22 (5), p.941-953
issn 1343-8875
1875-8975
language eng
recordid cdi_proquest_journals_2307440145
source SpringerLink Journals - AutoHoldings
subjects Classical and Continuum Physics
Computer Imaging
Engineering
Engineering Fluid Dynamics
Engineering Thermodynamics
Heat and Mass Transfer
Keywords
Pattern Recognition and Graphics
Regular Paper
Subject specialists
Supervisors
Vision
Visualization
title VISPubComPAS: a comparative analytical system for visualization publication data
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T01%3A27%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=VISPubComPAS:%20a%20comparative%20analytical%20system%20for%20visualization%20publication%20data&rft.jtitle=Journal%20of%20visualization&rft.au=Wang,%20Yang&rft.date=2019-10-01&rft.volume=22&rft.issue=5&rft.spage=941&rft.epage=953&rft.pages=941-953&rft.issn=1343-8875&rft.eissn=1875-8975&rft_id=info:doi/10.1007/s12650-019-00585-2&rft_dat=%3Cproquest_cross%3E2307440145%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2307440145&rft_id=info:pmid/&rfr_iscdi=true