Optimized Spatiotemporal Data Scheduling Based on Maximum Flow for Multilevel Visualization Tasks

Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different schedulin...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	ISPRS international journal of geo-information Ročník 9; číslo 9; s. 518
Hlavní autoři:	Zhu, Qing, Chen, Meite, Feng, Bin, Zhou, Yan, Li, Maosu, Xu, Zhaowen, Ding, Yulin, Liu, Mingwei, Wang, Wei, Xie, Xiao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.09.2020
Témata:	Adaptive algorithms Algorithms Caching Cloud computing cloud environment Computer simulation Data Data processing Efficiency Electronic data processing environment flow Global optimization Hardware Mathematical optimization Maximum flow methodology Methods Multilevel multilevel visualization tasks Optimization Priority scheduling Resources Scheduling Scheduling (Management) scheduling optimization Software spatial data Spatiotemporal data Task scheduling topology Visualization Visualization (Computers) wastes China
ISSN:	2220-9964, 2220-9964
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This paper proposes an optimized spatiotemporal data scheduling method based on maximum flow for multilevel visualization tasks. First, the spatiotemporal data scheduling framework is designed based on the analysis of three levels of visualization tasks. Second, the maximum flow model is introduced to construct the spatiotemporal data scheduling topological network, and the calculation algorithm of the maximum data flow is presented in detail. Third, according to the change in the data access hotspot, the adaptive caching algorithm and maximum flow model parameter switching strategy are devised to achieve task-driven spatiotemporal data optimization scheduling. Compared with two typical methods of first come first serve (FCFS) and priority scheduling algorithm (PSA) by simulating visualization tasks at three levels, the proposed maximum flow scheduling (MFS) method has been proven to be more flexible and efficient in adjusting each spatiotemporal data flow type as needed, and the method realizes spatiotemporal data flow global optimization under limited hardware resources in the cloud environment.
AbstractList	Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This paper proposes an optimized spatiotemporal data scheduling method based on maximum flow for multilevel visualization tasks. First, the spatiotemporal data scheduling framework is designed based on the analysis of three levels of visualization tasks. Second, the maximum flow model is introduced to construct the spatiotemporal data scheduling topological network, and the calculation algorithm of the maximum data flow is presented in detail. Third, according to the change in the data access hotspot, the adaptive caching algorithm and maximum flow model parameter switching strategy are devised to achieve task-driven spatiotemporal data optimization scheduling. Compared with two typical methods of first come first serve (FCFS) and priority scheduling algorithm (PSA) by simulating visualization tasks at three levels, the proposed maximum flow scheduling (MFS) method has been proven to be more flexible and efficient in adjusting each spatiotemporal data flow type as needed, and the method realizes spatiotemporal data flow global optimization under limited hardware resources in the cloud environment. Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This paper proposes an optimized spatiotemporal data scheduling method based on maximum flow for multilevel visualization tasks. First, the spatiotemporal data scheduling framework is designed based on the analysis of three levels of visualization tasks. Second, the maximum flow model is introduced to construct the spatiotemporal data scheduling topological network, and the calculation algorithm of the maximum data flow is presented in detail. Third, according to the change in the data access hotspot, the adaptive caching algorithm and maximum flow model parameter switching strategy are devised to achieve task-driven spatiotemporal data optimization scheduling. Compared with two typical methods of first come first serve (FCFS) and priority scheduling algorithm (PSA) by simulating visualization tasks at three levels, the proposed maximum flow scheduling (MFS) method has been proven to be more flexible and efficient in adjusting each spatiotemporal data flow type as needed, and the method realizes spatiotemporal data flow global optimization under limited hardware resources in the cloud environment. Keywords: scheduling optimization; maximum flow; spatiotemporal data; multilevel visualization tasks; cloud environment
Audience	Academic
Author	Zhou, Yan Feng, Bin Wang, Wei Liu, Mingwei Li, Maosu Xu, Zhaowen Ding, Yulin Chen, Meite Zhu, Qing Xie, Xiao
Author_xml	– sequence: 1 givenname: Qing surname: Zhu fullname: Zhu, Qing – sequence: 2 givenname: Meite surname: Chen fullname: Chen, Meite – sequence: 3 givenname: Bin surname: Feng fullname: Feng, Bin – sequence: 4 givenname: Yan surname: Zhou fullname: Zhou, Yan – sequence: 5 givenname: Maosu surname: Li fullname: Li, Maosu – sequence: 6 givenname: Zhaowen surname: Xu fullname: Xu, Zhaowen – sequence: 7 givenname: Yulin surname: Ding fullname: Ding, Yulin – sequence: 8 givenname: Mingwei surname: Liu fullname: Liu, Mingwei – sequence: 9 givenname: Wei surname: Wang fullname: Wang, Wei – sequence: 10 givenname: Xiao surname: Xie fullname: Xie, Xiao
BookMark	eNptkU9PFTEUxScGExFZ-QUmcWNiHvbvzHSJKEoCYQG6be5rb599dqZj21Hg01t4xhBiu2jT_M45uT0vm70pTtg0ryk54lyR93678YooIunwrNlnjJGVUp3Ye3R_0RzmvCV1KcoHQfYbuJyLH_0d2vZqhuJjwXGOCUL7EQq0V-Y72iX4adN-gFyhOLUXcOPHZWxPQ_zdupjaiyUUH_AXhvabzwsEf3fvNLXXkH_kV81zByHj4d_zoPl6-un65Mvq_PLz2cnx-coI3pWVYdQBRWYcRw5ybUjHnERHCUNliJWdNSh7LvqeUDsgCuuMdYwTYNg7zg-as52vjbDVc_IjpFsdweuHh5g2GlLxJqCuUUoqiTDItaCKg7WMCNn1a2uHal-93u685hR_LpiLHn02GAJMGJesmVADJ1R0rKJvnqDbuKSpTloproTkkspKHe2oDdR8P7lYEpi6LY7e1B5d_UB93AnKJBl6VQXvdgKTYs4J3b-JKNH3detHdVeaPqGNLw8d1Bgf_qv5A2Str5c
CitedBy_id	crossref_primary_10_1007_s13369_021_05603_w crossref_primary_10_3390_agriculture12091433
Cites_doi	10.1007/978-3-319-69137-4_28 10.3390/ijgi6010021 10.1016/j.future.2010.02.004 10.1080/13658816.2020.1749637 10.1016/j.neucom.2016.06.099 10.1109/CCGrid.2013.89 10.1016/j.jpdc.2019.01.005 10.1109/ACCESS.2018.2829142 10.1109/PDGC.2016.7913185 10.1016/j.compenvurbsys.2014.02.009 10.1109/CIVEMSA.2015.7158625 10.4401/ag-7268 10.1007/s41324-016-0080-4 10.1007/978-3-319-25691-7_12 10.1109/CCET.2018.8542359 10.1016/j.advengsoft.2016.01.009 10.1016/j.ins.2018.12.009 10.1109/MNET.2018.1700141 10.1109/TVCG.2013.226 10.3390/ijgi6040116 10.1016/j.isprsjprs.2015.12.003 10.1145/2628036 10.3390/ijgi6060165 10.1145/3139645.3139650 10.1007/978-3-319-12181-9_1 10.5194/isprsannals-II-4-W2-55-2015 10.1007/b138407 10.1109/PDP.2013.41 10.3390/ijgi7100393 10.1007/s11227-017-2012-z 10.1145/2788397 10.1109/ICRAECT.2017.28 10.1080/13658816.2017.1334897 10.1016/j.isprsjprs.2012.04.004 10.1109/EMEIT.2011.6024076 10.1007/s11806-011-0478-z 10.1007/BF02288321 10.1080/17538947.2018.1550122 10.3390/ijgi7090371 10.1109/CCGrid.2011.55
ContentType	Journal Article
Copyright	COPYRIGHT 2020 MDPI AG 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: COPYRIGHT 2020 MDPI AG – notice: 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION 7SC 7UA 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F1W FR3 H96 HCIFZ JQ2 KR7 L.G L6V L7M L~C L~D M7S P5Z P62 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7S9 L.6 DOA
DOI	10.3390/ijgi9090518
DatabaseName	CrossRef Computer and Information Systems Abstracts Water Resources Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central ProQuest Technology Collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central Korea ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources SciTech Premium Collection ProQuest Computer Science Collection Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database ProQuest advanced technologies & aerospace journals ProQuest Advanced Technologies & Aerospace Collection Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering collection AGRICOLA AGRICOLA - Academic Directory of Open Access Journals (DOAJ)
DatabaseTitle	CrossRef Publicly Available Content Database Aquatic Science & Fisheries Abstracts (ASFA) Professional Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China Water Resources Abstracts Environmental Sciences and Pollution Management Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection Natural Science Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Engineering Collection Advanced Technologies & Aerospace Collection Civil Engineering Abstracts Engineering Database ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources ProQuest One Academic UKI Edition ASFA: Aquatic Sciences and Fisheries Abstracts Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) AGRICOLA AGRICOLA - Academic
DatabaseTitleList	CrossRef AGRICOLA Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography Visual Arts
EISSN	2220-9964
ExternalDocumentID	oai_doaj_org_article_21f9595ea85b4193add204567bdd8701 A641250879 10_3390_ijgi9090518
GeographicLocations	China
GeographicLocations_xml	– name: China
GroupedDBID	5VS 8FE 8FG 8FH AADQD AAFWJ AAHBH AAYXX ABJCF ADBBV ADMLS AENEX AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION GROUPED_DOAJ HCIFZ IAO KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PQGLB PROAC PTHSS ZBA ITC 7SC 7UA 8FD ABUWG AZQEC C1K DWQXO F1W FR3 H96 JQ2 KR7 L.G L7M L~C L~D PKEHL PQEST PQQKQ PQUKI PRINS 7S9 L.6 PUEGO
ID	FETCH-LOGICAL-c436t-c21fa1e2cf3e3a5bc062f5ef102e9c0d56dce57347701d8ee4dfcdf230a2e7f33
IEDL.DBID	BENPR
ISICitedReferencesCount	3
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000580789900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2220-9964
IngestDate	Fri Oct 03 12:33:27 EDT 2025 Fri Sep 05 08:36:36 EDT 2025 Fri Jul 25 12:03:52 EDT 2025 Tue Nov 04 18:00:22 EST 2025 Tue Nov 18 19:49:09 EST 2025 Sat Nov 29 07:12:57 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	9
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c436t-c21fa1e2cf3e3a5bc062f5ef102e9c0d56dce57347701d8ee4dfcdf230a2e7f33
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
OpenAccessLink	https://www.proquest.com/docview/2439453515?pq-origsite=%requestingapplication%
PQID	2439453515
PQPubID	2032387
ParticipantIDs	doaj_primary_oai_doaj_org_article_21f9595ea85b4193add204567bdd8701 proquest_miscellaneous_2498301462 proquest_journals_2439453515 gale_infotracacademiconefile_A641250879 crossref_primary_10_3390_ijgi9090518 crossref_citationtrail_10_3390_ijgi9090518
PublicationCentury	2000
PublicationDate	2020-09-01
PublicationDateYYYYMMDD	2020-09-01
PublicationDate_xml	– month: 09 year: 2020 text: 2020-09-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	ISPRS international journal of geo-information
PublicationYear	2020
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Mingwei (ref_46) 2018; 47 Arslan (ref_42) 2019; 126 ref_12 ref_11 ref_10 Kharouf (ref_34) 2017; 25 ref_53 ref_51 Trubka (ref_6) 2016; 117 Yuan (ref_36) 2010; 26 ref_17 Ma (ref_27) 2018; 6 ref_15 Li (ref_26) 2014; 2 Calciu (ref_33) 2017; 51 ref_24 ref_22 ref_21 ref_20 Bhat (ref_32) 2011; 3 Pan (ref_25) 2017; 73 Ramkumar (ref_41) 2019; 12 (ref_4) 2012; 71 ref_28 Zhao (ref_13) 2018; 507 Su (ref_16) 2016; 95 Zhang (ref_23) 2018; 32 Ferreira (ref_18) 2013; 19 Wang (ref_9) 2018; 274 Goldfarb (ref_50) 1988; 13 ref_35 ref_31 ref_30 Goldberg (ref_48) 2014; 57 ref_39 Liu (ref_1) 2017; 31 Zhang (ref_7) 2011; 14 ref_37 Li (ref_29) 2017; 61 Song (ref_14) 2015; 2 Zhan (ref_38) 2015; 47 ref_45 ref_44 ref_43 ref_40 ref_3 ref_2 Li (ref_54) 2018; 2 ref_49 Qing (ref_47) 2017; 46 Gaillard (ref_19) 2020; 13 ref_8 ref_5 Zhu (ref_52) 2020; 49
References_xml	– ident: ref_49 – ident: ref_37 doi: 10.1007/978-3-319-69137-4_28 – ident: ref_2 doi: 10.3390/ijgi6010021 – volume: 26 start-page: 1200 year: 2010 ident: ref_36 article-title: A data placement strategy in scientific cloud workflows publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2010.02.004 – ident: ref_20 doi: 10.1080/13658816.2020.1749637 – ident: ref_39 – volume: 274 start-page: 88 year: 2018 ident: ref_9 article-title: Spatial query based virtual reality GIS analysis platform publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.06.099 – ident: ref_30 doi: 10.1109/CCGrid.2013.89 – volume: 126 start-page: 134 year: 2019 ident: ref_42 article-title: Scheduling opportunities for asymmetrically reliable caches publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2019.01.005 – volume: 6 start-page: 27010 year: 2018 ident: ref_27 article-title: An improved web cache replacement algorithm based on weighting and cost publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2829142 – ident: ref_28 doi: 10.1109/PDGC.2016.7913185 – volume: 61 start-page: 163 year: 2017 ident: ref_29 article-title: A replication strategy for a distributed high-speed caching system based on spatiotemporal access patterns of geospatial data publication-title: Comput. Environ. Urban Syst. doi: 10.1016/j.compenvurbsys.2014.02.009 – volume: 3 start-page: 594 year: 2011 ident: ref_32 article-title: Cloud Computing: A solution to Geographical Information Systems (GIS) publication-title: Int. J. Comput. Sci. Eng. – ident: ref_8 – ident: ref_11 doi: 10.1109/CIVEMSA.2015.7158625 – ident: ref_35 doi: 10.4401/ag-7268 – volume: 25 start-page: 89 year: 2017 ident: ref_34 article-title: An integrated architectural framework for geoprocessing in cloud environment publication-title: Spat. Inf. Res. doi: 10.1007/s41324-016-0080-4 – ident: ref_45 doi: 10.1007/978-3-319-25691-7_12 – ident: ref_10 doi: 10.1109/CCET.2018.8542359 – volume: 95 start-page: 7 year: 2016 ident: ref_16 article-title: Multi-dimensional visualization of large-scale marine hydrological environmental data publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2016.01.009 – volume: 507 start-page: 472 year: 2018 ident: ref_13 article-title: Relational granulation method based on quotient space theory for maximum flow problem publication-title: Inf. Sci. doi: 10.1016/j.ins.2018.12.009 – volume: 32 start-page: 140 year: 2018 ident: ref_23 article-title: A Multi-Level Cache Framework for Remote Resource Access in Transparent Computing publication-title: IEEE Netw. doi: 10.1109/MNET.2018.1700141 – volume: 12 start-page: 77 year: 2019 ident: ref_41 article-title: Preserving security using crisscross AES and FCFS scheduling in cloud computing publication-title: Int. J. Adv. Intell. Paradig. – volume: 19 start-page: 2149 year: 2013 ident: ref_18 article-title: Visual exploration of big spatio-temporal urban data: A study of new york city taxi trips publication-title: IEEE Trans. Vis. Comput. Graph. doi: 10.1109/TVCG.2013.226 – ident: ref_17 doi: 10.3390/ijgi6040116 – volume: 117 start-page: 175 year: 2016 ident: ref_6 article-title: A web-based 3D visualisation and assessment system for urban precinct scenario modelling publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2015.12.003 – volume: 57 start-page: 82 year: 2014 ident: ref_48 article-title: Efficient maximum flow algorithms publication-title: Commun. Acm doi: 10.1145/2628036 – ident: ref_3 – ident: ref_24 doi: 10.3390/ijgi6060165 – volume: 51 start-page: 24 year: 2017 ident: ref_33 article-title: How to implement any concurrent data structure for modern servers publication-title: Acm Sigops Oper. Syst. Rev. doi: 10.1145/3139645.3139650 – volume: 2 start-page: 136 year: 2018 ident: ref_54 article-title: ECharts: A declarative framework for rapid construction of web-based visualization publication-title: Vis. Inform. – ident: ref_5 doi: 10.1007/978-3-319-12181-9_1 – volume: 2 start-page: 55 year: 2015 ident: ref_14 article-title: Building spatiotemporal cloud platform for supporting GIS application publication-title: ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. doi: 10.5194/isprsannals-II-4-W2-55-2015 – ident: ref_44 doi: 10.1007/b138407 – ident: ref_40 doi: 10.1109/PDP.2013.41 – ident: ref_21 – volume: 2 start-page: 133 year: 2014 ident: ref_26 article-title: A Replacement Strategy for A Distributed Caching System Based on The Spatiotemporal Access Pattern of Geospatial Data publication-title: ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. – ident: ref_22 doi: 10.3390/ijgi7100393 – volume: 73 start-page: 4324 year: 2017 ident: ref_25 article-title: An enhanced active caching strategy for data-intensive computations in distributed GIS publication-title: J. Supercomput. doi: 10.1007/s11227-017-2012-z – volume: 47 start-page: 63 year: 2015 ident: ref_38 article-title: Cloud computing resource scheduling and a survey of its evolutionary approaches publication-title: ACM Comput. Surv. doi: 10.1145/2788397 – ident: ref_51 doi: 10.1109/ICRAECT.2017.28 – volume: 46 start-page: 1672 year: 2017 ident: ref_47 article-title: The review of visual analysis methods of multi-modal spatio-temporal big data publication-title: Acta Geod. Cartogr. Sin. – volume: 31 start-page: 1891 year: 2017 ident: ref_1 article-title: Optimization of simulation and visualization analysis of dam-failure flood disaster for diverse computing systems publication-title: Int. J. Geogr. Inf. Sci. doi: 10.1080/13658816.2017.1334897 – volume: 71 start-page: 12 year: 2012 ident: ref_4 article-title: CityGML–Interoperable semantic 3D city models publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2012.04.004 – ident: ref_12 – ident: ref_43 doi: 10.1109/EMEIT.2011.6024076 – ident: ref_15 – volume: 14 start-page: 150 year: 2011 ident: ref_7 article-title: GeoScope: Full 3D geospatial information system case study publication-title: Geo-Spat. Inf. Sci. doi: 10.1007/s11806-011-0478-z – volume: 13 start-page: 81 year: 1988 ident: ref_50 article-title: A computational comparison of the dinic and network simplex methods for maximum flow publication-title: Ann. Oper. Res. doi: 10.1007/BF02288321 – volume: 13 start-page: 627 year: 2020 ident: ref_19 article-title: Visualisation and personalisation of multi-representations city models publication-title: Int. J. Digit. Earth doi: 10.1080/17538947.2018.1550122 – ident: ref_53 doi: 10.3390/ijgi7090371 – ident: ref_31 doi: 10.1109/CCGrid.2011.55 – volume: 49 start-page: 681 year: 2020 ident: ref_52 article-title: An efficient sparse graph index method for dynamic and associated data publication-title: Acta Geodaetica et Cartographica Sinica – volume: 47 start-page: 1098 year: 2018 ident: ref_46 article-title: The multi-level visualization task model for multi-modal spatio-temporal data publication-title: Acta Geod. Cartogr. Sin.
SSID	ssj0000913840
Score	2.1762557
Snippet	Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading,...
SourceID	doaj proquest gale crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	518
SubjectTerms	Adaptive algorithms Algorithms Caching Cloud computing cloud environment Computer simulation Data Data processing Efficiency Electronic data processing environment flow Global optimization Hardware Mathematical optimization Maximum flow methodology Methods Multilevel multilevel visualization tasks Optimization Priority scheduling Resources Scheduling Scheduling (Management) scheduling optimization Software spatial data Spatiotemporal data Task scheduling topology Visualization Visualization (Computers) wastes
SummonAdditionalLinks	– databaseName: Directory of Open Access Journals (DOAJ) dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3di9QwEA9yCOqD6KlYPSXCgSCUa5smaR7v1MUXT-FOubeQj8nZc7cr112__nonaW5ZQfHF13YI6Xwk86MzvyFkH0wdKsOhrJh0ZQuWl7bjvAytZKEOltWmSsMm5PFxd3am3m-N-oo1YRM98KS4g6YOiisOpuO2xWwD4zEyqAtpvUdfS8CnkmoLTKUzWNUMocvUkMcQ1x_0F-e9qiIbVffbFZSY-v92HqdLZnaH3M7ZIT2cdnWXXINhl9zIg8o__dgltz7243qSGO8R8w4DftH_BE9PUmV0Jpqa01dmZegJGsTHSvNzeoSXlafLgb413_vFekFn8-U3igkrTR2481g6RKe1c2MmPTXj5_E--TB7ffryTZlnJpSuZWJVOtSXqaFxgQEz3LpKNIFDwDwClKs8F94Bl6yVqDvfAbQ-OB8QiJgGZGDsAdkZlgM8JLTxzBnPubAIqRx01gqoa3BSQBuUFQV5caVG7TKheJxrMdcILKLO9ZbOC7K_Ef4y8Wj8Wewo2mMjEsmv0wN0CZ1dQv_LJQryPFpTxxDFDTmTOw3wsyLZlT4ULaZ1VSdVQfauDK5z7I66ic3CnGGiV5Bnm9cYdfFXihlguY4yqotgVDSP_seOH5ObTcTxqXZtj-ysLtfwhFx3X1f9ePk0ufYvszz_ZQ priority: 102 providerName: Directory of Open Access Journals
Title	Optimized Spatiotemporal Data Scheduling Based on Maximum Flow for Multilevel Visualization Tasks
URI	https://www.proquest.com/docview/2439453515 https://www.proquest.com/docview/2498301462 https://doaj.org/article/21f9595ea85b4193add204567bdd8701
Volume	9
WOSCitedRecordID	wos000580789900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: DOA dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: P5Z dateStart: 20120301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: PCBAR dateStart: 20120301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: M7S dateStart: 20120301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: BENPR dateStart: 20120301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2220-9964 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913840 issn: 2220-9964 databaseCode: PIMPY dateStart: 20120301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwELZgFwk48FgWUVgqI62EhBRtEsd5nNAWWsFhS0QXtHCJHHtcAm2zNC2vA7-dmcQtIAEXLj4kI8vWjOdhz3zD2CGowPpKgueLRHsRlNIrUyk9GyXCBrYUgfLbZhPJeJyenWW5u3BrXFrlRie2itrUmu7Ij0Iq4ZQCze_j848edY2i11XXQuMi2yWkMpTz3cFwnL_c3rIQ6iWGMF1hnsD4_qh6P60yn1Cp0t9MUYvY_ze93Bqb0fX_XeYNds25mfy4k4ub7AIs9thl1_H83dc9dvV11aw7iuYWUy9Qc8yrb2D4pE2xdohVM_5UrRSfIGcNpaxP-QCtnuH1gp-oL9V8PeejWf2Zo-fL21LeGeUg8W5uV-HJT1Xzodlnr0bD0yfPPNd8wdORiFeeDgOrAgi1FSCULLUfh1aCRYcEMu0bGRsNMhFRkviBSQEiY7WxGNGoEBIrxG22s6gXcIfx0AitjJRxibGZhrQsYwgC0EkMkc3KuMcebfhQaIdMTg0yZgVGKMS04hem9djhlvi8A-T4M9mAGLolIRTt9kO9nBbuUBa4xUxmElQqywjFB3U9ofPHSWkM6rGgxx6SOBR01nFBWrmSBdwWoWYVx3GE_qGfJlmPHWzEoXBKoCl-ykKPPdj-xuNLbzJqAfWaaLKUoto4vPvvKe6xKyGF-m162wHbWS3XcJ9d0p9WVbPsO7nvt1cKfUpgndD4fYhjLt_i__z5Sf7mB3zPFUA
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB5VKVLhwKOACBRYpCIkJKu21-vHAaGWEjVqEyI1oPZk1vsIhiQucUIpP4rfyIztBJCAWw9c7ZHl9X7-5rHzANg20rOuFMZxeaScwGTCyWIhHBtE3Ho24550q2ETUb8fn5wkgzX4vqyFobTKJSdWRK0LRTHyHZ9KOAVH9fvy7LNDU6PodHU5QqOGxaG5OEeXrXzR3cf9fer7ndfDVwdOM1XAUQEP547yPSs94yvLDZciU27oW2EsalqTKFeLUCsjIh5Ekevp2JhAW6UtmurSN5GlAChS_nqAYHdbsD7o9ganq6gOddlEl6kuBOQ8cXfyj6M8cakLVvyb6qsmBPxND1TKrXPjf_ssN-F6Y0az3Rr3t2DNTDdho5no_uFiE669y8tFLVHeBvkGmXGSfzOaHVcp5E1HrjHbl3PJjhG5mlLyR2wPtbpmxZT15Nd8spiwzrg4Z2jZs6pUeUw5Vqx-dlPByoay_FTegbeXst670JoWU3MPmK-5klqIMEPfU5k4y0LjeUZFoQlskoVteL7c91Q1nddpAMg4RQ-MQJL-ApI2bK-Ez-qGI38W2yMArUSoS3h1oZiN0oZ0UlxiIhJhZCyyAC111GU0fSCMMq2Rp702PCP4pcRl-EJKNiUZuCzqCpbuhgHav24cJW3YWsIvbUiuTH9irw1PVreRnujMSU5NsSCZJCavPfTv__sRj2HjYNg7So-6_cMHcNWnsEaVyrcFrflsYR7CFfVlnpezR80_x-D9ZeP5BwQLb18
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9NAFB5VKWI5sBRQAwUGqQgJyYrt8XjsA0INaURVCBEtVW9mPEtqSOISJ5Ty0_h1vGdPAkjArQeu9tPIY3_-3jJvIWTbyMD6khvPZ0J5kcm5lyecezYSzAY2Z4H062ETYjBIjo_T4Rr5vqyFwbTKJSfWRK1LhTHyToglnJyB-u1YlxYx7PVfnH72cIIUnrQux2k0ENk352fgvlXP93rwrZ-EYX_38OUrz00Y8FTE4rmnwsDKwITKMsMkz5Ufh5YbC1rXpMrXPNbKcMEiIfxAJ8ZE2iptwWyXoREWg6FA_-uCgdPTIuvd3cHw3SrCgx03wX1qigIZS_1O8XFUpD52xEp-U4P1tIC_6YRa0fVv_M-v6Ca57sxrutP8D7fImplukCtu0vvJ-Qa5dlRUi0aiuk3kW2DMSfHNaHpQp5a7Tl1j2pNzSQ8A0RpT9Ue0C9pe03JK38ivxWQxof1xeUbB4qd1CfMYc69os7arbKWHsvpU3SHvL2S_d0lrWk7NJqGhZkpqzuMcfFJlkjyPTRAYJWIT2TSP2-TZEgOZch3ZcTDIOAPPDAGT_QKYNtleCZ82jUj-LNZFMK1EsHt4faGcjTJHRhlsMeUpNzLheQQWPOg4nEoQi1xr4O-gTZ4iFDPkOHggJV2pBmwLu4VlO3EEdrGfiLRNtpZQzBz5VdlPHLbJ49VtoC08i5JTUy5QJk3Qm4_De_9e4hG5DCDOXu8N9u-TqyFGO-oMvy3Sms8W5gG5pL7Mi2r20P1-lHy4aDj_ADwcd_k
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=Optimized+Spatiotemporal+Data+Scheduling+Based+on+Maximum+Flow+for+Multilevel+Visualization+Tasks&rft.jtitle=ISPRS+international+journal+of+geo-information&rft.au=Zhu%2C+Qing&rft.au=Chen%2C+Meite&rft.au=Feng%2C+Bin&rft.au=Zhou%2C+Yan&rft.date=2020-09-01&rft.pub=MDPI+AG&rft.eissn=2220-9964&rft.volume=9&rft.issue=9&rft.spage=518&rft_id=info:doi/10.3390%2Fijgi9090518&rft.externalDBID=HAS_PDF_LINK
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2220-9964&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2220-9964&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2220-9964&client=summon