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 language | English |
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Title of host publication | Lecture Notes in Computer Science |
Editors | O. Gervasi, M.L. Gavrilova, V. Kumar, A. Lagana, H.P. Lee, Y. Mun, D. Taniar, C.J.K. Tan |
Pages | 1225-1234 |
Number of pages | 10 |
Volume | 3481 |
Edition | II |
Publication status | Published - 2005 |
Event | International Conference on Computational Science and Its Applications - ICCSA 2005 - , Singapore Duration: 2005 May 9 → 2005 May 12 |
Other
Other | International Conference on Computational Science and Its Applications - ICCSA 2005 |
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Country/Territory | Singapore |
Period | 05/5/9 → 05/5/12 |
ASJC Scopus subject areas
- Computer Science (miscellaneous)