Abstract
In this chapter, the authors propose an approach to predict uncertain spatiotemporal data. This approach is unique in the predicting element nodes which are integrated into the position element node in uncertain spatiotemporal XML data tree. At the same time, the other element nodes do not need to make any changes. In addition, the authors apply this method to meteorological applications and established a series of experimental models for testing. PGX (predictive model with grey model based on XML), which is applied to uncertain spatiotemporal objects, is able to achieve the minimum mean accuracy of 0.5% in a short time. The experimental results show that PGX can effectively improve the efficiency of information storage and retrieval. The experimental prediction accuracy is guaranteed (the relative error is between 0.5% and 5%) and the query time based on XML is 89.2% shorter than that of SQL Server.