Visualization and Visual Analysis of Ensemble Data: A Survey
Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing visualization needs from a variety of disciplines. From the data analysis point of view, it can be observed that many ensemble visualization works fo...
Saved in:
Published in | IEEE transactions on visualization and computer graphics Vol. 25; no. 9; pp. 2853 - 2872 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
IEEE
01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing visualization needs from a variety of disciplines. From the data analysis point of view, it can be observed that many ensemble visualization works focus on the same facet of ensemble data, use similar data aggregation or uncertainty modeling methods. However, the lack of reflections on those essential commonalities and a systematic overview of those works prevents visualization researchers from effectively identifying new or unsolved problems and planning for further developments. In this paper, we take a holistic perspective and provide a survey of ensemble visualization. Specifically, we study ensemble visualization works in the recent decade, and categorize them from two perspectives: (1) their proposed visualization techniques; and (2) their involved analytic tasks. For the first perspective, we focus on elaborating how conventional visualization techniques (e.g., surface, volume visualization techniques) have been adapted to ensemble data; for the second perspective, we emphasize how analytic tasks (e.g., comparison, clustering) have been performed differently for ensemble data. From the study of ensemble visualization literature, we have also identified several research trends, as well as some future research opportunities. |
---|---|
AbstractList | Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing visualization needs from a variety of disciplines. From the data analysis point of view, it can be observed that many ensemble visualization works focus on the same facet of ensemble data, use similar data aggregation or uncertainty modeling methods. However, the lack of reflections on those essential commonalities and a systematic overview of those works prevents visualization researchers from effectively identifying new or unsolved problems and planning for further developments. In this paper, we take a holistic perspective and provide a survey of ensemble visualization. Specifically, we study ensemble visualization works in the recent decade, and categorize them from two perspectives: (1) their proposed visualization techniques; and (2) their involved analytic tasks. For the first perspective, we focus on elaborating how conventional visualization techniques (e.g., surface, volume visualization techniques) have been adapted to ensemble data; for the second perspective, we emphasize how analytic tasks (e.g., comparison, clustering) have been performed differently for ensemble data. From the study of ensemble visualization literature, we have also identified several research trends, as well as some future research opportunities. Not provided. Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing visualization needs from a variety of disciplines. From the data analysis point of view, it can be observed that many ensemble visualization works focus on the same facet of ensemble data, use similar data aggregation or uncertainty modeling methods. However, the lack of reflections on those essential commonalities and a systematic overview of those works prevents visualization researchers from effectively identifying new or unsolved problems and planning for further developments. In this paper, we take a holistic perspective and provide a survey of ensemble visualization. Specifically, we study ensemble visualization works in the recent decade, and categorize them from two perspectives: (1) their proposed visualization techniques; and (2) their involved analytic tasks. For the first perspective, we focus on elaborating how conventional visualization techniques (e.g., surface, volume visualization techniques) have been adapted to ensemble data; for the second perspective, we emphasize how analytic tasks (e.g., comparison, clustering) have been performed differently for ensemble data. From the study of ensemble visualization literature, we have also identified several research trends, as well as some future research opportunities.Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing visualization needs from a variety of disciplines. From the data analysis point of view, it can be observed that many ensemble visualization works focus on the same facet of ensemble data, use similar data aggregation or uncertainty modeling methods. However, the lack of reflections on those essential commonalities and a systematic overview of those works prevents visualization researchers from effectively identifying new or unsolved problems and planning for further developments. In this paper, we take a holistic perspective and provide a survey of ensemble visualization. Specifically, we study ensemble visualization works in the recent decade, and categorize them from two perspectives: (1) their proposed visualization techniques; and (2) their involved analytic tasks. For the first perspective, we focus on elaborating how conventional visualization techniques (e.g., surface, volume visualization techniques) have been adapted to ensemble data; for the second perspective, we emphasize how analytic tasks (e.g., comparison, clustering) have been performed differently for ensemble data. From the study of ensemble visualization literature, we have also identified several research trends, as well as some future research opportunities. |
Author | Li, Cheng Shen, Han-Wei Wang, Junpeng Hazarika, Subhashis |
Author_xml | – sequence: 1 givenname: Junpeng orcidid: 0000-0002-1130-9914 surname: Wang fullname: Wang, Junpeng email: wang.7665@osu.edu organization: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA – sequence: 2 givenname: Subhashis orcidid: 0000-0003-0575-9318 surname: Hazarika fullname: Hazarika, Subhashis email: hazarika.3@osu.edu organization: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA – sequence: 3 givenname: Cheng orcidid: 0000-0002-3114-9399 surname: Li fullname: Li, Cheng email: li.4076@osu.edu organization: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA – sequence: 4 givenname: Han-Wei surname: Shen fullname: Shen, Han-Wei email: shen.94@osu.edu organization: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29994615$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/1610947$$D View this record in Osti.gov |
BookMark | eNp9kU1v1DAQhi1URD_gByAkFNELlyzjj_ij4rJaSkGqxIHSq-U4E-Eqa5c4QVp-fb1ky6EHTjMaPe9o5n1PyVFMEQl5TWFFKZgPN7ebqxUDqldMN1wx-oycUCNoDQ3Io9KDUjWTTB6T05zvAKgQ2rwgx8wYIyRtTsjH25BnN4Q_bgopVi521TKp1tENuxxylfrqMmbctgNWn9zkLqp19X0ef-PuJXneuyHjq0M9Iz8-X95svtTX366-btbXtedKTjXnqFrlsHW-ZcJ4yVXXom6Nd5xpD7L3Al3TI1JohEHeo1Qd9wCCdcg5PyPvlr0pT8FmHyb0P32KEf1kqSxWCFWg9wt0P6ZfM-bJbkP2OAwuYpqzZSA1FyAbKOj5E_QuzWP5t1BMasGF5qxQbw_U3G6xs_dj2LpxZx_NK4BaAD-mnEfsbbnsr4_T6MJgKdh9THYfk93HZA8xFSV9onxc_j_Nm0UTEPEfrwU0xTP-AAynmvc |
CODEN | ITVGEA |
CitedBy_id | crossref_primary_10_1016_j_cag_2022_10_008 crossref_primary_10_1109_TVCG_2021_3140153 crossref_primary_10_1016_j_bdr_2021_100251 crossref_primary_10_1016_j_jbi_2021_103941 crossref_primary_10_1177_14738716221147289 crossref_primary_10_3389_fbinf_2022_793819 crossref_primary_10_1109_TVCG_2021_3061925 crossref_primary_10_1016_j_cag_2021_04_010 crossref_primary_10_1111_cgf_14333 crossref_primary_10_3390_ijgi9010019 crossref_primary_10_1016_j_cag_2022_01_007 crossref_primary_10_3390_a18010039 crossref_primary_10_1111_cgf_14537 crossref_primary_10_1109_MCG_2022_3176325 crossref_primary_10_3390_su151914340 crossref_primary_10_1109_TVCG_2022_3209379 crossref_primary_10_1111_cgf_13806 crossref_primary_10_1007_s12650_019_00608_y crossref_primary_10_1016_j_energy_2022_125939 crossref_primary_10_1109_TVCG_2020_2994954 crossref_primary_10_1109_TVCG_2024_3357065 crossref_primary_10_1109_TVCG_2024_3350076 crossref_primary_10_1016_j_visinf_2025_02_001 crossref_primary_10_1016_j_cag_2022_04_005 crossref_primary_10_1177_14738716221126992 crossref_primary_10_1109_TVCG_2022_3189094 crossref_primary_10_1111_cgf_14386 crossref_primary_10_1016_j_visinf_2022_10_003 crossref_primary_10_1016_j_visinf_2022_10_002 crossref_primary_10_1109_TVCG_2023_3326855 crossref_primary_10_1111_cgf_14785 crossref_primary_10_1111_cgf_14544 crossref_primary_10_1109_TVCG_2024_3456393 crossref_primary_10_1111_cgf_14029 crossref_primary_10_1111_cgf_14548 crossref_primary_10_1002_itl2_284 crossref_primary_10_1109_TVCG_2022_3209464 crossref_primary_10_1098_rsta_2019_0431 crossref_primary_10_1007_s11242_023_02019_y crossref_primary_10_1109_TVCG_2020_3030377 crossref_primary_10_1109_MCG_2022_3169554 crossref_primary_10_1080_17538947_2024_2431624 crossref_primary_10_1007_s00287_019_01222_w crossref_primary_10_1109_TVCG_2022_3165345 crossref_primary_10_1016_j_cag_2023_06_006 crossref_primary_10_1109_TBDATA_2021_3092174 crossref_primary_10_1111_cgf_15084 crossref_primary_10_1007_s00371_024_03435_x crossref_primary_10_1111_cgf_15083 crossref_primary_10_1109_TVCG_2020_3022359 crossref_primary_10_1111_cgf_14432 crossref_primary_10_1111_cgf_13985 crossref_primary_10_1109_TVCG_2022_3180899 crossref_primary_10_2139_ssrn_4188415 crossref_primary_10_1109_TVCG_2022_3209356 crossref_primary_10_1016_j_cag_2020_07_001 crossref_primary_10_1016_j_gvc_2020_200014 crossref_primary_10_1109_TVCG_2021_3101418 crossref_primary_10_3390_fire7070227 crossref_primary_10_1007_s12650_019_00612_2 crossref_primary_10_1007_s12650_024_00999_7 crossref_primary_10_5194_gmd_16_4617_2023 crossref_primary_10_1109_TBDATA_2023_3324482 crossref_primary_10_1016_j_epidem_2022_100574 crossref_primary_10_1109_TVCG_2024_3456338 crossref_primary_10_1007_s12650_021_00755_1 crossref_primary_10_1002_met_1916 crossref_primary_10_1109_ACCESS_2023_3286304 |
Cites_doi | 10.1109/TVCG.2016.2607204 10.1109/VISUAL.2001.964550 10.1109/TVCG.2014.2346744 10.1111/j.1467-8659.2009.01677.x 10.1109/TVCG.2010.190 10.1109/PACIFICVIS.2016.7465251 10.1145/1838544.1838551 10.1109/TVCG.2010.20 10.1109/SC.2014.87 10.1109/TVCG.2016.2598830 10.1109/TVCG.2013.147 10.1109/TVCG.2015.2467198 10.1007/s003710050111 10.1109/LDAV.2015.7348068 10.1109/TVCG.2013.141 10.1111/cgf.12100 10.1111/cgf.12359 10.1111/j.1467-8659.2012.03112.x 10.1109/PacificVis.2012.6183556 10.1615/Int.J.UncertaintyQuantification.2012003966 10.1007/978-1-4471-6497-5_1 10.1145/637357.637361 10.1109/ICDMW.2009.55 10.1109/TVCG.2014.2346455 10.1111/j.1467-8659.2012.03096.x 10.1109/TVCG.2014.2346448 10.1111/cgf.12390 10.1109/TVCG.2009.155 10.1109/TVCG.2010.111 10.1109/TVCG.2010.181 10.1109/PACIFICVIS.2016.7465271 10.1145/2818517.2818531 10.1111/j.1467-8659.2011.01942.x 10.1111/cgf.12653 10.1109/TVCG.2016.2534560 10.1109/MCG.2005.71 10.1109/VAST.2015.7347635 10.1111/j.1467-8659.2011.01944.x 10.1109/TVCG.2015.2507569 10.1109/TVCG.2017.2745178 10.1109/VAST.2015.7347634 10.1109/MCG.2010.55 10.1109/LDAV.2011.6092313 10.1109/TVCG.2015.2410278 10.1109/PacificVis.2012.6183591 10.1109/VISUAL.2003.1250412 10.1109/TVCG.2013.144 10.1109/TVCG.2017.2744099 10.1109/PACIFICVIS.2016.7465272 10.1109/TVCG.2015.2507592 10.2481/dsj.3.153 10.1093/oso/9780198524847.001.0001 10.1016/S0097-8493(02)00055-9 10.1111/cgf.12649 10.1007/BF00547132 10.1177/1473871611416549 10.1109/TVCG.2006.76 10.1109/TVCG.2015.2498554 10.1109/TVCG.2013.92 10.3354/cr01213 10.1126/science.1115255 10.1109/TVCG.2016.2598868 10.1111/j.1467-8659.2012.03095.x 10.1017/S0022112095000462 10.1109/TVCG.2017.2743989 10.1109/TVCG.2011.203 10.1007/s12650-016-0388-0 10.1109/TVCG.2004.39 10.1007/s00791-002-0079-3 10.1109/PACIFICVIS.2016.7465256 10.1109/TVCG.2013.143 10.1109/PACIFICVIS.2011.5742374 10.1109/TVCG.2012.110 10.1109/MCG.2014.52 10.1109/WSC.2007.4419664 10.1109/TVCG.2016.2598870 10.1615/Int.J.UncertaintyQuantification.2012003934 10.1109/PACIFICVIS.2017.8031589 10.1088/1742-6596/180/1/012089 10.1109/MCG.2015.70 10.1109/TIP.2003.819861 10.1109/TVCG.2016.2598869 10.1111/j.1467-8659.2012.03097.x 10.1109/TVCG.2015.2467204 10.1109/PACIFICVIS.2017.8031586 10.1109/TVCG.2013.208 10.1109/TVCG.2014.2346755 10.1109/SciVis.2015.7429487 10.1109/TVCG.2014.2307892 10.1109/TVCG.2015.2467958 10.1109/PACIFICVIS.2017.8031590 10.1109/TVCG.2010.247 10.1109/TVCG.2014.2346321 10.1109/TVCG.2016.2637333 10.1109/TVCG.2013.138 10.1109/TVCG.2015.2468093 10.1111/cgf.12898 10.1111/j.1467-8659.2009.01604.x 10.1109/TVCG.2010.171 10.1109/VIZSEC.2015.7312766 10.1007/s00357-003-0004-6 10.1109/MCG.2003.1231171 |
ContentType | Journal Article |
Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
CorporateAuthor | The Ohio State Univ., Columbus, OH (United States) |
CorporateAuthor_xml | – name: The Ohio State Univ., Columbus, OH (United States) |
DBID | 97E RIA RIE AAYXX CITATION NPM 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 OTOTI |
DOI | 10.1109/TVCG.2018.2853721 |
DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic OSTI.GOV |
DatabaseTitle | CrossRef PubMed Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional MEDLINE - Academic |
DatabaseTitleList | PubMed Technology Research Database MEDLINE - Academic |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering Computer Science |
EISSN | 1941-0506 |
EndPage | 2872 |
ExternalDocumentID | 1610947 29994615 10_1109_TVCG_2018_2853721 8405549 |
Genre | orig-research Research Support, U.S. Gov't, Non-P.H.S Journal Article |
GrantInformation_xml | – fundername: Los Alamos National Laboratory grantid: 47145 funderid: 10.13039/100008902 – fundername: U.S. Department of Energy grantid: DE-SC0007444; DE-DC0012495 funderid: 10.13039/100000015 – fundername: UT-Battelle LLC grantid: 4000159447 – fundername: NSF grantid: IIS-1250752; IIS-1065025 |
GroupedDBID | --- -~X .DC 0R~ 29I 4.4 53G 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNS TN5 AAYXX CITATION 5VS AETIX AGSQL AI. AIBXA ALLEH H~9 IFJZH NPM RIG RNI RZB VH1 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 OTOTI PQEST RIC |
ID | FETCH-LOGICAL-c376t-33e7b7aebacb249c637dbe8b9ca328c06fc4ea5fee10549e3fe67d3c0042de333 |
IEDL.DBID | RIE |
ISSN | 1077-2626 1941-0506 |
IngestDate | Fri May 19 01:10:19 EDT 2023 Fri Jul 11 06:35:40 EDT 2025 Sun Jun 29 13:35:54 EDT 2025 Mon Jul 21 06:00:03 EDT 2025 Tue Jul 01 03:58:52 EDT 2025 Thu Apr 24 22:54:51 EDT 2025 Wed Aug 27 02:54:35 EDT 2025 |
IsDoiOpenAccess | false |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 9 |
Language | English |
License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c376t-33e7b7aebacb249c637dbe8b9ca328c06fc4ea5fee10549e3fe67d3c0042de333 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 SC0007444 USDOE Office of Science (SC) |
ORCID | 0000-0002-3114-9399 0000-0002-1130-9914 0000-0003-0575-9318 0000000305759318 0000000231149399 0000000211309914 |
PMID | 29994615 |
PQID | 2268434832 |
PQPubID | 75741 |
PageCount | 20 |
ParticipantIDs | crossref_citationtrail_10_1109_TVCG_2018_2853721 proquest_journals_2268434832 ieee_primary_8405549 osti_scitechconnect_1610947 pubmed_primary_29994615 proquest_miscellaneous_2068340650 crossref_primary_10_1109_TVCG_2018_2853721 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2019-09-01 |
PublicationDateYYYYMMDD | 2019-09-01 |
PublicationDate_xml | – month: 09 year: 2019 text: 2019-09-01 day: 01 |
PublicationDecade | 2010 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States – name: New York |
PublicationTitle | IEEE transactions on visualization and computer graphics |
PublicationTitleAbbrev | TVCG |
PublicationTitleAlternate | IEEE Trans Vis Comput Graph |
PublicationYear | 2019 |
Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
References | ref57 ref59 ref58 ref53 ref52 ref55 (ref56) 2017 hunt (ref94) 1988 ref51 ref50 ref46 ref45 ref41 ref44 ref43 senin (ref88) 2008 ref49 ref8 ref7 yu (ref120) 2010; 30 matkovi? (ref54) 2010 ref4 ref6 phadke (ref47) 2012 ref5 ester (ref86) 1996 ref100 ref101 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 liu (ref9) 2012 ref39 mahalanobis (ref90) 1936 ref38 otto (ref115) 2011 diggle (ref3) 2002 jarema (ref21) 2016; 24 ref24 ref23 cha (ref89) 2007; 1 ref26 ref25 ref20 ref28 ref27 ref29 ref13 ref12 ref15 ref14 ref97 ref96 ref99 ref11 ref98 ref10 matkovi? (ref22) 2009; 15 ref17 ref16 ref19 ref18 ref93 ref92 ref95 höllt (ref48) 2013 ref91 bishop (ref84) 2006 ref85 ref87 pfaffelmoser (ref66) 2013 höllt (ref103) 2013 ref82 (ref42) 2017 ref81 ref83 ref80 ref79 ref108 ref78 ref109 ref106 ref75 ref104 ref74 ref105 ref77 ref102 ref76 ref2 ref1 alabi (ref73) 2012 ref71 ref111 ref70 ref112 ref72 ref68 bürger (ref110) 2012 ref119 ref67 ref117 ref69 guo (ref107) 2013; 19 ref118 ref64 ref63 ref116 ref113 ref65 ref114 ref60 ref62 ref61 |
References_xml | – ident: ref101 doi: 10.1109/TVCG.2016.2607204 – ident: ref105 doi: 10.1109/VISUAL.2001.964550 – ident: ref117 doi: 10.1109/TVCG.2014.2346744 – start-page: 73 year: 2012 ident: ref9 article-title: Gaussian mixture model based volume visualization publication-title: Proc IEEE Symp Large Data Anal Vis – ident: ref85 doi: 10.1111/j.1467-8659.2009.01677.x – ident: ref14 doi: 10.1109/TVCG.2010.190 – ident: ref19 doi: 10.1109/PACIFICVIS.2016.7465251 – ident: ref57 doi: 10.1145/1838544.1838551 – ident: ref113 doi: 10.1109/TVCG.2010.20 – ident: ref67 doi: 10.1109/SC.2014.87 – ident: ref25 doi: 10.1109/TVCG.2016.2598830 – ident: ref99 doi: 10.1109/TVCG.2013.147 – ident: ref29 doi: 10.1109/TVCG.2015.2467198 – year: 2017 ident: ref42 article-title: National hurricane center, uncertainty cone. – ident: ref28 doi: 10.1007/s003710050111 – ident: ref55 doi: 10.1109/LDAV.2015.7348068 – ident: ref49 doi: 10.1109/TVCG.2013.141 – ident: ref69 doi: 10.1111/cgf.12100 – start-page: 185 year: 2013 ident: ref48 article-title: Visual analysis of uncertainties in ocean forecasts for planning and operation of off-shore structures publication-title: Proc IEEE Pacific Vis Symp – ident: ref100 doi: 10.1111/cgf.12359 – ident: ref24 doi: 10.1111/j.1467-8659.2012.03112.x – ident: ref60 doi: 10.1109/PacificVis.2012.6183556 – ident: ref50 doi: 10.1615/Int.J.UncertaintyQuantification.2012003966 – ident: ref26 doi: 10.1007/978-1-4471-6497-5_1 – ident: ref1 doi: 10.1145/637357.637361 – ident: ref23 doi: 10.1109/ICDMW.2009.55 – ident: ref6 doi: 10.1109/TVCG.2014.2346455 – ident: ref10 doi: 10.1111/j.1467-8659.2012.03096.x – ident: ref78 doi: 10.1109/TVCG.2014.2346448 – ident: ref112 doi: 10.1111/cgf.12390 – volume: 15 start-page: 1351 year: 2009 ident: ref22 article-title: Interactive visual analysis of complex scientific data as families of data surfaces publication-title: IEEE Trans Vis Comput Graph doi: 10.1109/TVCG.2009.155 – start-page: 55 year: 2013 ident: ref66 article-title: Visualizing contour distributions in 2d ensemble data publication-title: EuroVis - Short Papers – ident: ref62 doi: 10.1109/TVCG.2010.111 – ident: ref43 doi: 10.1109/TVCG.2010.181 – ident: ref74 doi: 10.1109/PACIFICVIS.2016.7465271 – ident: ref108 doi: 10.1145/2818517.2818531 – ident: ref12 doi: 10.1111/j.1467-8659.2011.01942.x – ident: ref30 doi: 10.1111/cgf.12653 – ident: ref106 doi: 10.1109/TVCG.2016.2534560 – ident: ref33 doi: 10.1109/MCG.2005.71 – ident: ref82 doi: 10.1109/VAST.2015.7347635 – ident: ref72 doi: 10.1111/j.1467-8659.2011.01944.x – ident: ref8 doi: 10.1109/TVCG.2015.2507569 – ident: ref83 doi: 10.1109/TVCG.2017.2745178 – ident: ref63 doi: 10.1109/VAST.2015.7347634 – start-page: 82 940p year: 2012 ident: ref110 article-title: Instant visitation maps for interactive visualization of uncertain particle trajectories publication-title: Proc IS&T/SPIE Electronic Imaging 97 – volume: 30 start-page: 45 year: 2010 ident: ref120 article-title: In situ visualization for large-scale combustion simulations publication-title: IEEE Comput Graph Appl doi: 10.1109/MCG.2010.55 – ident: ref2 doi: 10.1109/LDAV.2011.6092313 – ident: ref64 doi: 10.1109/TVCG.2015.2410278 – year: 2017 ident: ref56 article-title: Scientific visualization contest 2016 – ident: ref102 doi: 10.1109/PacificVis.2012.6183591 – ident: ref76 doi: 10.1109/VISUAL.2003.1250412 – year: 1988 ident: ref94 article-title: Eddies, streams, and convergence zones in turbulent flows – start-page: 49 year: 1936 ident: ref90 article-title: On the generalised distance in statistics publication-title: Proc Nat Inst Sci India – volume: 19 start-page: 2733 year: 2013 ident: ref107 article-title: Coupled ensemble flow line advection and analysis publication-title: IEEE Trans Vis Comput Graph doi: 10.1109/TVCG.2013.144 – ident: ref13 doi: 10.1109/TVCG.2017.2744099 – ident: ref77 doi: 10.1109/PACIFICVIS.2016.7465272 – ident: ref18 doi: 10.1109/TVCG.2015.2507592 – ident: ref38 doi: 10.2481/dsj.3.153 – year: 2002 ident: ref3 publication-title: Analysis of Longitudinal Data doi: 10.1093/oso/9780198524847.001.0001 – start-page: 82 940b year: 2012 ident: ref47 article-title: Exploring ensemble visualization publication-title: Proc IS&T/SPIE Electronic Imaging 97 – volume: 1 year: 2007 ident: ref89 article-title: Comprehensive survey on distance/similarity measures between probability density functions publication-title: city – ident: ref59 doi: 10.1016/S0097-8493(02)00055-9 – ident: ref52 doi: 10.1111/cgf.12649 – ident: ref41 doi: 10.1007/BF00547132 – ident: ref79 doi: 10.1177/1473871611416549 – ident: ref93 doi: 10.1109/TVCG.2006.76 – ident: ref16 doi: 10.1109/TVCG.2015.2498554 – ident: ref104 doi: 10.1109/TVCG.2013.92 – ident: ref39 doi: 10.3354/cr01213 – ident: ref36 doi: 10.1126/science.1115255 – ident: ref15 doi: 10.1109/TVCG.2016.2598868 – ident: ref11 doi: 10.1111/j.1467-8659.2012.03095.x – start-page: 69 year: 2013 ident: ref103 article-title: Extraction and visual analysis of seismic horizon ensembles publication-title: Proc Eurographics '05 (short papers) – ident: ref95 doi: 10.1017/S0022112095000462 – ident: ref111 doi: 10.1109/TVCG.2017.2743989 – ident: ref51 doi: 10.1109/TVCG.2011.203 – ident: ref109 doi: 10.1007/s12650-016-0388-0 – ident: ref80 doi: 10.1109/TVCG.2004.39 – ident: ref61 doi: 10.1007/s00791-002-0079-3 – ident: ref81 doi: 10.1109/PACIFICVIS.2016.7465256 – ident: ref7 doi: 10.1109/TVCG.2013.143 – ident: ref97 doi: 10.1109/PACIFICVIS.2011.5742374 – start-page: 82 940u year: 2012 ident: ref73 article-title: Comparative visualization of ensembles using ensemble surface slicing publication-title: Proc IS&T/SPIE Electronic Imaging 97 – ident: ref34 doi: 10.1109/TVCG.2012.110 – ident: ref35 doi: 10.1109/MCG.2014.52 – ident: ref44 doi: 10.1109/WSC.2007.4419664 – ident: ref32 doi: 10.1109/TVCG.2016.2598870 – start-page: 1 year: 2008 ident: ref88 article-title: Dynamic time warping algorithm review – ident: ref114 doi: 10.1615/Int.J.UncertaintyQuantification.2012003934 – start-page: 87 year: 2011 ident: ref115 article-title: Closed stream lines in uncertain vector fields publication-title: Proc 27th Spring Conf Comput Graph – ident: ref75 doi: 10.1109/PACIFICVIS.2017.8031589 – ident: ref45 doi: 10.1088/1742-6596/180/1/012089 – ident: ref65 doi: 10.1109/MCG.2015.70 – ident: ref87 doi: 10.1109/TIP.2003.819861 – ident: ref40 doi: 10.1109/TVCG.2016.2598869 – volume: 24 start-page: 25 year: 2016 ident: ref21 article-title: Comparative visual analysis of transport variability in flow ensembles publication-title: J WSCG – ident: ref116 doi: 10.1111/j.1467-8659.2012.03097.x – ident: ref5 doi: 10.1109/TVCG.2015.2467204 – ident: ref119 doi: 10.1109/PACIFICVIS.2017.8031586 – ident: ref71 doi: 10.1109/TVCG.2013.208 – start-page: 226 year: 1996 ident: ref86 article-title: A density-based algorithm for discovering clusters in large spatial databases with noise publication-title: Proc Int'l Conf Knowledge Discovery and Data Mining – ident: ref37 doi: 10.1109/TVCG.2014.2346755 – ident: ref46 doi: 10.1109/SciVis.2015.7429487 – year: 2006 ident: ref84 publication-title: Pattern Recognition and Machine Learning – ident: ref20 doi: 10.1109/TVCG.2014.2307892 – ident: ref70 doi: 10.1109/TVCG.2015.2467958 – ident: ref118 doi: 10.1109/PACIFICVIS.2017.8031590 – ident: ref68 doi: 10.1109/TVCG.2010.247 – ident: ref98 doi: 10.1109/TVCG.2014.2346321 – ident: ref58 doi: 10.1109/TVCG.2016.2637333 – ident: ref92 doi: 10.1109/TVCG.2013.138 – start-page: 1 year: 2010 ident: ref54 article-title: Interactive visual analysis of families of surfaces: An application to car race and car setup publication-title: Proc Symp Vis Analytics Sci Technol – ident: ref17 doi: 10.1109/TVCG.2015.2468093 – ident: ref4 doi: 10.1111/cgf.12898 – ident: ref96 doi: 10.1111/j.1467-8659.2009.01604.x – ident: ref53 doi: 10.1109/TVCG.2010.171 – ident: ref31 doi: 10.1109/VIZSEC.2015.7312766 – ident: ref91 doi: 10.1007/s00357-003-0004-6 – ident: ref27 doi: 10.1109/MCG.2003.1231171 |
SSID | ssj0014489 |
Score | 2.5860975 |
SecondaryResourceType | review_article |
Snippet | Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing... Not provided. |
SourceID | osti proquest pubmed crossref ieee |
SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
StartPage | 2853 |
SubjectTerms | Biological system modeling Clustering Computational modeling Computer Science Data analysis Data management Data models Data visualization Ensemble data literature analysis Task analysis taxonomy Uncertainty Visualization visualization and visual analysis |
Title | Visualization and Visual Analysis of Ensemble Data: A Survey |
URI | https://ieeexplore.ieee.org/document/8405549 https://www.ncbi.nlm.nih.gov/pubmed/29994615 https://www.proquest.com/docview/2268434832 https://www.proquest.com/docview/2068340650 https://www.osti.gov/biblio/1610947 |
Volume | 25 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3Lb9YwDLe2neDAa8DKBgoSJ0S_dU2-NkFcpj2YkMaFbdotysOVEFuL9rVI8Ndj96UJDcSpVZuqaWwnPzf2zwBvKq-XmCOmhD6qlCY8neogTRqlM7EqtAp9utjp5-LkXH26XF6uwbs5FwYR--AzXPBpv5cfm9Dxr7JdckZo9TPrsE6O25CrNe8YkJthhvjCMs0JpY87mHuZ2T27OPjIQVx6kdPiRC4PMwATMFIFF8O9tRz19VXo0JB1_R1x9ivP8UM4nfo8BJx8W3StX4Rff9A5_u9HPYIHIwQV-4POPIY1rJ_A_VvEhJvw4eLrirMthxxN4eoohitiIjERTSWO6hVe-ysUh65178W--NLd_MCfT-H8-Ojs4CQd6yykgaaXNpUSS1869C548sZCIcvoUXsTnMx1yIoqKHTLCpHAmDIoKyzKKAMbfEQp5TPYqJsat0CoEI3TTKFTZMop4zPMc19yESztY1YmkE3DbcNIQs61MK5s74xkxrKwLAvLjsJK4O38yPeBgeNfjTd5hOeG4-AmsM0ytYQqmBo3cAxRaO0ec80r6tTOJGo7WvDK5kyDIxVNeAm8nm-T7fGGiqux6ahNVmipGOQm8HxQkfnNk4K9uLtH23CP-j1Gq-3ARnvT4UuCN61_1ev1bwi28YU |
linkProvider | IEEE |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwzV1Lb9QwELaqcgAOvMojtICR4IKUbWp7ExvBoeqDLX1c2Fa9ubYzkVDbBHWzoPJb-Cv8N2byUoWAWyVOWWW9Wif-PP7GnvmGsVeF12MQADGyjyJGg6djHaSJc-lMXqRahSZdbP8gnRyqj8fj4wX2Y8iFAYAm-AxG9LE5y8-rMKetslV0RnD1M10I5S5cfkMHbfZ-ZxNH87UQ21vTjUnc1RCIA06dOpYSMp858C549DRCKrPcg_YmOCl0SNIiKHDjAgCJhjIgC0izXAYCcw6StjvRwN9AnjEWbXbYcEaBjo1pIxqzWKBf0J2ZriVmdXq08YHCxvRI4HKIThZpDiMVUymV372yADYVXfBS4Xz-O8dt1rrtu-xn_5baEJfT0bz2o_D9NwHJ__U13mN3OpLN19tZcZ8tQPmA3b4ivbjE3h19nlE-aZuFyl2Z8_YO72VaeFXwrXIG5_4M-Kar3Vu-zj_NL77C5UN2eC3df8QWy6qEJ4yrkBunSSQoTZRTxicghM-ozJf2eZJFLOmH14ZOZp2qfZzZxt1KjCVwWAKH7cARsTfDT760GiP_arxEIzo07AYzYsuEIYu8icR_A0VJhdqukZq-wk6t9NCynY2aWUFCP1KhSY_Yy-FrtC50ZORKqObYJkm1VETjI_a4heTwzz2gn_65Ry_Yzcl0f8_u7RzsLrNb-AxdbN4KW6wv5vAMyVztnzdzirOT60bfL_35UhM |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Visualization+and+Visual+Analysis+of+Ensemble+Data%3A+A+Survey&rft.jtitle=IEEE+transactions+on+visualization+and+computer+graphics&rft.au=Wang%2C+Junpeng&rft.au=Hazarika%2C+Subhashis&rft.au=Li%2C+Cheng&rft.au=Shen%2C+Han-Wei&rft.date=2019-09-01&rft.issn=1941-0506&rft.eissn=1941-0506&rft.volume=25&rft.issue=9&rft.spage=2853&rft_id=info:doi/10.1109%2FTVCG.2018.2853721&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1077-2626&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1077-2626&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1077-2626&client=summon |