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Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyJrSantMace:2017:ViAnPr
TitleVisual Analysis of Predictive Suffix Trees for Discovering Movement Patterns and Behaviors
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size4006 KiB
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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
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-21 22:01:04 :: -> administrator ::
2020-02-19 02:01:35 :: administrator -> :: 2017
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
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.
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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