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Predicting Uncertain Spatiotemporal XML Data Integrated With Grey Dynamic Model

Predicting Uncertain Spatiotemporal XML Data Integrated With Grey Dynamic Model

Copyright: © 2024 |Pages: 49
ISBN13: 9781668491089|ISBN10: 1668491087|ISBN13 Softcover: 9781668491126|EISBN13: 9781668491096
DOI: 10.4018/978-1-6684-9108-9.ch016
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MLA

Luyi Bai and Lin Zhu. "Predicting Uncertain Spatiotemporal XML Data Integrated With Grey Dynamic Model." Uncertain Spatiotemporal Data Management for the Semantic Web, IGI Global, 2024, pp.324-372. https://doi.org/10.4018/978-1-6684-9108-9.ch016

APA

L. Bai & L. Zhu (2024). Predicting Uncertain Spatiotemporal XML Data Integrated With Grey Dynamic Model. IGI Global. https://doi.org/10.4018/978-1-6684-9108-9.ch016

Chicago

Luyi Bai and Lin Zhu. "Predicting Uncertain Spatiotemporal XML Data Integrated With Grey Dynamic Model." In Uncertain Spatiotemporal Data Management for the Semantic Web. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/978-1-6684-9108-9.ch016

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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.

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