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

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

Luyi Bai (Northeastern University, China), Nan Li (Northeastern University, China), Chengjia Sun (Northeastern University, China) and Yuan Zhao (Northeastern University, China)
DOI: 10.4018/978-1-5225-8446-9.ch008
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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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Spatiotemporal data is a kind of special data which can contain time information and spatial information simultaneously (Pfoser, Tryfona, & Jensen, 2005). Spatiotemporal data has the characteristics of multi-source, large scale and fast update. It can describe the information of the object more accurately because it contains the attributes of time and space. It also can look up the characteristic of the stage behavior of the object in real-time, and can observe and predict the probability of the occurrence of a specific stage behavior with reference to the spatiotemporal association constraint model. Spatiotemporal database is a complex system to store and process spatiotemporal data, and it is an important branch in the field of database querying. However, spatial database and temporal database are independent of each other and there is no intersection of both research fields before 1990s. With the development of temporal database and spatial database, researchers found the relationship between the two kinds of databases and combined the two to study gradually, resulting in a spatiotemporal database. The content of spatiotemporal database is very complex and huge, which can be used to manage spatiotemporal data. It can express the time and change, and can solve the problem of storage and management of spatiotemporal data in general database. Therefore, the spatiotemporal database has more research value than other forms of databases, and can be applied to a wider range of fields, such as the meteorological information management systems (Kurte, Durbha, King, Younan, & Potnis, 2017), environmental changes monitoring (Li et al., 2017), vehicle detection and tracking (Huang, Lee, & Lin., 2017; Ramanathan, & Chen, 2017), video surveillance (Hampapur et al., 2005), online estimation of temperature (Xu, Li, & Liu, 2018), even gesture recognition (Zhang, Zhu, Shen, & Song, 2017), spatiotemporal distribution of birds (Ferreira et al., 2011), and so on.

Key Terms in this Chapter

Transition Matrix: A Markov transition matrix is a square matrix describing the probabilities of moving from one state to another in a dynamic system. In each row are the probabilities of moving from the state represented by that row, to the other states. Thus, the rows of a Markov transition matrix each add to one.

Markov Chain: A Markov chain is a random process that undergoes transitions from one state to another on a state space.

Spatiotemporal Data: Spatiotemporal data is a kind of special data which can contain time information and spatial information simultaneously.

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