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Prediction of Uncertain Spatiotemporal Data Based on XML Integrated With Markov Chain

Prediction of Uncertain Spatiotemporal Data Based on XML Integrated With Markov Chain
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Author(s): Luyi Bai (Northeastern University, China), Nan Li (Northeastern University, China), Chengjia Sun (Northeastern University, China) and Yuan Zhao (Northeastern University, China)
Copyright: 2019
Pages: 30
Source title: Emerging Technologies and Applications in Data Processing and Management
Source Author(s)/Editor(s): Zongmin Ma (Nanjing University of Aeronautics and Astronautics, China) and Li Yan (Nanjing University of Aeronautics and Astronautics, China)
DOI: 10.4018/978-1-5225-8446-9.ch008

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Abstract

Since XML could benefit data management greatly and Markov chains have an advantage in data prediction, the authors study the methodology of predicting uncertain spatiotemporal data based on XML integrated with Markov chain. To accomplish this, first, the researchers devise an uncertain spatiotemporal data model based on XML. Then, the researchers put forward the method based on Markov chains to predict spatiotemporal data, which has taken the uncertainty into consideration. Next, the researchers apply the prediction method to meteorological field. Finally, the experimental results demonstrate the advantages the authors approach. Such a method of prediction could broaden the research field of spatiotemporal data, and provide a significant reference in the study of forecasting uncertain spatiotemporal data.

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