A stochastic viewpoint on the generation of spatiotemporal datasets

MoonBae Song, KwangJin Park, Ki S. Kong, Sang-Geun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The issue of standardized generation scheme of spatio-temporal datasets is a research area of growing importance. In case of the lack of large real datasets, especially, benchmarking spatio-temporal database requires the generation of synthetic datasets simulating the real-word behavior of spatial objects that move and evolve over time. Recently, a few studies have been conducted on the generation of artificial datasets from a different point of view. For more realistic datasets, this paper proposes a novel framework, called state-based movement frame-work (SMF) to provide more generalized framework for both describing and generating the movement of complexly moving objects which simulate the movement of real-life objects. Based on Markov chain model, a well-known stochastic model, the proposed model classifies the whole trajectory of a moving object into a set of movement state. From some illustrative examples, we show that the proposed scheme is able to generate various realistic datasets with respect to the given input parameters.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science
EditorsO. Gervasi, M.L. Gavrilova, V. Kumar, A. Lagana, H.P. Lee, Y. Mun, D. Taniar, C.J.K. Tan
Pages1225-1234
Number of pages10
Volume3481
EditionII
Publication statusPublished - 2005
EventInternational Conference on Computational Science and Its Applications - ICCSA 2005 - , Singapore
Duration: 2005 May 92005 May 12

Other

OtherInternational Conference on Computational Science and Its Applications - ICCSA 2005
CountrySingapore
Period05/5/905/5/12

Fingerprint

Benchmarking
Stochastic models
Markov processes
Trajectories

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Cite this

Song, M., Park, K., Kong, K. S., & Lee, S-G. (2005). A stochastic viewpoint on the generation of spatiotemporal datasets. In O. Gervasi, M. L. Gavrilova, V. Kumar, A. Lagana, H. P. Lee, Y. Mun, D. Taniar, ... C. J. K. Tan (Eds.), Lecture Notes in Computer Science (II ed., Vol. 3481, pp. 1225-1234)

A stochastic viewpoint on the generation of spatiotemporal datasets. / Song, MoonBae; Park, KwangJin; Kong, Ki S.; Lee, Sang-Geun.

Lecture Notes in Computer Science. ed. / O. Gervasi; M.L. Gavrilova; V. Kumar; A. Lagana; H.P. Lee; Y. Mun; D. Taniar; C.J.K. Tan. Vol. 3481 II. ed. 2005. p. 1225-1234.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Song, M, Park, K, Kong, KS & Lee, S-G 2005, A stochastic viewpoint on the generation of spatiotemporal datasets. in O Gervasi, ML Gavrilova, V Kumar, A Lagana, HP Lee, Y Mun, D Taniar & CJK Tan (eds), Lecture Notes in Computer Science. II edn, vol. 3481, pp. 1225-1234, International Conference on Computational Science and Its Applications - ICCSA 2005, Singapore, 05/5/9.
Song M, Park K, Kong KS, Lee S-G. A stochastic viewpoint on the generation of spatiotemporal datasets. In Gervasi O, Gavrilova ML, Kumar V, Lagana A, Lee HP, Mun Y, Taniar D, Tan CJK, editors, Lecture Notes in Computer Science. II ed. Vol. 3481. 2005. p. 1225-1234
Song, MoonBae ; Park, KwangJin ; Kong, Ki S. ; Lee, Sang-Geun. / A stochastic viewpoint on the generation of spatiotemporal datasets. Lecture Notes in Computer Science. editor / O. Gervasi ; M.L. Gavrilova ; V. Kumar ; A. Lagana ; H.P. Lee ; Y. Mun ; D. Taniar ; C.J.K. Tan. Vol. 3481 II. ed. 2005. pp. 1225-1234
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