Interpolation and Prediction of Spatiotemporal Data Based on XML Integrated with Grey Dynamic ModelReportar como inadecuado


Interpolation and Prediction of Spatiotemporal Data Based on XML Integrated with Grey Dynamic Model


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1

College of Information Science & Engineering, Northeastern University, Shenyang 110819, China

2

Qinhuangdao Branch Campus, Northeastern University, Qinhuangdao 066004, China

3

School of Software, Northeastern University, Shenyang 110819, China

4

College of Computer Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China





*

Author to whom correspondence should be addressed.



Abstract Interpolation and prediction of spatiotemporal data are integral components of many real-world applications. Thus, approaches of interpolating and predicting spatiotemporal data have been extensively investigated. Currently, the grey dynamic model has been used to enhance the performance of interpolating and predicting spatiotemporal data. Meanwhile, the Extensible Markup Language XML has unique characteristics of information representation and exchange. In this paper, we first couple the grey dynamic model with the spatiotemporal XML model. Based on a definition of the position part of the spatiotemporal XML model, we extract the corresponding position information of each time interval and propose an algorithm for constructing an AVL tree to store them. Then, we present the architecture of an interpolating and predicting process and investigate change operations in positions. On this basis, we present an algorithm for interpolation and prediction of spatiotemporal data based on XML integrated with the grey dynamic model. Experimental results demonstrate the performance advantages of the proposed approach. View Full-Text

Keywords: spatiotemporal Extensible Markup Language XML; grey dynamic model; AVL tree; interpolation and prediction spatiotemporal Extensible Markup Language XML; grey dynamic model; AVL tree; interpolation and prediction





Autor: Luyi Bai 1,2,* , Li Yan 3,4 and Zongmin Ma 1,4

Fuente: http://mdpi.com/



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