Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PFRDSH |
Repository | sid.inpe.br/sibgrapi/2017/08.21.22.01 |
Last Update | 2017:08.21.22.01.04 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/08.21.22.01.04 |
Metadata Last Update | 2020:02.19.02.01.35 administrator |
Citation Key | JrSantMace:2017:ViAnPr |
Title | Visual Analysis of Predictive Suffix Trees for Discovering Movement Patterns and Behaviors  |
Format | On-line |
Year | 2017 |
Date | Oct. 17-20, 2017 |
Access Date | 2021, Jan. 21 |
Number of Files | 1 |
Size | 4006 KiB |
Context area | |
Author | 1 Junior, Antonio Jose Melo Leite 2 Santos, Emanuele 3 vidal, Creto Augusto 4 Macedo, Jose Antonio Fernandes de |
Affiliation | 1 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 |
Editor | Torchelsen, 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 Address | melojr@virtual.ufc.br |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2017-08-21 22:01:04 :: melojr@virtual.ufc.br -> administrator :: 2020-02-19 02:01:35 :: administrator -> :: 2017 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | Visual Analysis, Movement Pattern, Predictive Suffix Trees. |
Abstract | The 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. |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PFRDSH |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PFRDSH |
Language | en |
Target File | PID4960307.pdf |
User Group | melojr@virtual.ufc.br |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PJT9LS 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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