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Computing and visualizing popular places

Data analysis and knowledge discovery in trajectory databases is an emerging field with a growing number of applications such as managing traffic, planning tourism infrastructures, analyzing professional sport matches or better understanding wildlife. A well-known collection of patterns which can occur for a subset of trajectories of moving objects exists. In this paper, we study the popular places pattern, that is, locations that are visited by many moving objects. We consider two criteria, strong and weak, to establish either the exact number of times that an object has visited a place during its complete trajectory or whether it has visited the place, or not. To solve the problem of reporting popular places, we introduce the popularity map. The popularity of a point is a measure of how many times the moving objects of a set have visited that point. The popularity map is the subdivision, into regions, of a plane where all the points have the same popularity. We propose different algorithms to efficiently compute and visualize popular places, the so-called popular regions and their schematization, by taking advantage of the parallel computing capabilities of the graphics processing units. Finally, we provide and discuss the experimental results obtained with the implementation of our algorithms

We thank the reviewers for their suggestions and comments. Authors are partially supported by the Spanish MCI grant TIN2010-20590-C02-02

© Knowledge and Information Systems, 2014, vol. 40, núm. 2, p. 411-437

Springer

Author: Fort, Marta
Sellarès i Chiva, Joan Antoni
Valladares Cereceda, Ignacio
Date: 2014
Abstract: Data analysis and knowledge discovery in trajectory databases is an emerging field with a growing number of applications such as managing traffic, planning tourism infrastructures, analyzing professional sport matches or better understanding wildlife. A well-known collection of patterns which can occur for a subset of trajectories of moving objects exists. In this paper, we study the popular places pattern, that is, locations that are visited by many moving objects. We consider two criteria, strong and weak, to establish either the exact number of times that an object has visited a place during its complete trajectory or whether it has visited the place, or not. To solve the problem of reporting popular places, we introduce the popularity map. The popularity of a point is a measure of how many times the moving objects of a set have visited that point. The popularity map is the subdivision, into regions, of a plane where all the points have the same popularity. We propose different algorithms to efficiently compute and visualize popular places, the so-called popular regions and their schematization, by taking advantage of the parallel computing capabilities of the graphics processing units. Finally, we provide and discuss the experimental results obtained with the implementation of our algorithms
We thank the reviewers for their suggestions and comments. Authors are partially supported by the Spanish MCI grant TIN2010-20590-C02-02
Format: application/pdf
ISSN: 0219-1377 (versió paper)
0219-3116 (versió electrònica)
Document access: http://hdl.handle.net/10256/11975
Language: eng
Publisher: Springer
Collection: MICINN/PN 2011-2013/TIN2010-20590-C02-02
Reproducció digital del document publicat a: http://dx.doi.org/10.1007/s10115-013-0639-5
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Is part of: © Knowledge and Information Systems, 2014, vol. 40, núm. 2, p. 411-437
Rights: Tots els drets reservats
Subject: Geometria computacional
Computational geometry
Title: Computing and visualizing popular places
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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