An Approach for Predicting Uncertain Spatiotemporal XML Data Integrated With Grey Dynamic Model

Query and prediction have been proved to be one of the most important operations for uncertain spatiotemporal data and deserve further study. In this paper, we propose an approach to predict uncertain spatiotemporal data, which is intended to integrate the grey dynamic model into the extensible markup language (XML). Our approach is unique in the predicting element nodes which are integrated into the position element node in uncertain spatiotemporal XML data tree, and at the same time, the other element nodes do not need to make any changes. In addition, we applied our method to a meteorological application and established a series of experimental models for testing. The experimental results show that our method is accurate and useful. The model of prediction with grey model based on XML (PGX), 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.