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1. Identity statement
Reference TypeBook Section
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3PF785P
Repositorysid.inpe.br/mtc-m21b/2017/08.17.18.26   (restricted access)
Last Update2017:12.19.15.03.43 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21b/2017/08.17.18.26.01
Metadata Last Update2018:06.04.02.27.44 (UTC) administrator
Secondary KeyINPE--/
ISBN978-331962406-8
Citation KeyMonteiroFerrSant:2017:AlDiPa
TitleAn algorithm to discover partners in trajectories
Year2017
Access Date2024, Apr. 26
Secondary TypePRE LI
Number of Files1
Size4375 KiB
2. Context
Author1 Monteiro, Diego Vilela
2 Ferreira, Karine Reis
3 Santos, Rafael Duarte Coelho dos
Resume Identifier1
2 8JMKD3MGP5W/3C9JHKN
3 8JMKD3MGP5W/3C9JJ4N
Group1 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
2 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
3 LABAC-COCTE-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 dvm1607@gmail.com
2 karine.ferreira@inpe.br
3 rafael.santos@inpe.br
EditorGervasi, Osvaldo
Murgante, Beniamino
Misra, Sanjay
Borruso, Giuseppe
Torre, Carmelo M.
Rocha, Ana Maria A. C.
Taniar, David
Apduhan, Bernady O.
Stankova, Elena
Cuzzocrea, Alfredo
Book TitleComputational Science and Its Applications – ICCSA 2017
PublisherSpringer
Pages647-661
Series TitleLecture Notes in Computer Science , 10409
History (UTC)2017-08-17 18:27:10 :: simone -> administrator :: 2017
2018-01-09 11:46:32 :: administrator -> simone :: 2017
2018-01-09 18:21:07 :: simone -> administrator :: 2017
2018-06-04 02:27:44 :: administrator -> simone :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsMoving objects
Trajectory
Pattern
Data mining
AbstractSpatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many researches on moving object trajectory data mining. In this paper, we propose an efficient method to discover partners in moving object trajectories. Such method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. We present two case studies using the proposed algorithm.
AreaCOMP
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > An algorithm to...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > An algorithm to...
Arrangement 3urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > An algorithm to...
doc Directory Contentaccess
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agreement Directory Content
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4. Conditions of access and use
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User Groupself-uploading-INPE-MCTI-GOV-BR
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Linking8JMKD3MGP3W34P/3P8ANQ2
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/3F2PHGS
DisseminationBNDEPOSITOLEGAL
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
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