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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFRDSH
Repositorysid.inpe.br/sibgrapi/2017/08.21.22.01
Last Update2017:08.21.22.01.04 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.21.22.01.04
Metadata Last Update2022:06.14.00.08.58 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.20
Citation KeyJrSantMace:2017:ViAnPr
TitleVisual Analysis of Predictive Suffix Trees for Discovering Movement Patterns and Behaviors
FormatOn-line
Year2017
Access Date2024, Apr. 29
Number of Files1
Size4006 KiB
2. Context
Author1 Junior, Antonio Jose Melo Leite
2 Santos, Emanuele
3 vidal, Creto Augusto
4 Macedo, Jose Antonio Fernandes de
Affiliation1 Virtual University Institute - Federal University of Ceara - Fortaleza, Brazil
2 Department of Computing - Federal University of Ceara - Fortaleza, Brazil
3 Department of Computing - Federal University of Ceara - Fortaleza, Brazil
4 Department of Computing - Federal University of Ceara - Fortaleza, Brazil
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressmelojr@virtual.ufc.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-21 22:01:04 :: melojr@virtual.ufc.br -> administrator ::
2022-06-14 00:08:58 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsVisual Analysis
Movement Pattern
Predictive Suffix Trees
AbstractThe use of GPS-equipped devices has allowed generating and storing data related to massive amounts of moving objects, promoting many solutions to movement prediction problems. Movement prediction became essential to perform tasks in several areas ranging from analysis of the popularity of geographic regions; and management of traffic and transportation; to recommendations in location-based social networks. To explore this type of data is a complex task because one must deal simultaneously with space, time and probability. In this work, we apply the branching time concept to visual analytics, proposing an approach that supports movement prediction using Probabilistic Suffix Trees. We try to substitute the traditional evaluation method, based on reading texts, by an interactive visual solution. To validate the proposed solution, we developed and tested a visualization tool using a real dataset. It assisted experts to quickly identify where a person lives, where she works and to recognize some of her movement patterns and probable behaviors.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Visual Analysis of...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Visual Analysis of...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFRDSH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFRDSH
Languageen
Target FilePID4960307.pdf
User Groupmelojr@virtual.ufc.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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