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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Song, M. B., Park, K. J., Kong, K. S., & Lee, S. K. (2005). A stochastic viewpoint on the generation of spatiotemporal datasets. In Lecture Notes in Computer Science (Vol. 3481, pp. 1225–1234). Springer Verlag. https://doi.org/10.1007/11424826_130
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