Interpolation and prediction of spatiotemporal data are integral components of many real-world applications. Thus, approaches to interpolating and predicting spatiotemporal data have been extensively investigated. Currently, the grey dynamic model has been used to enhance the performance of interpolating and predicting spatiotemporal data. Meanwhile, the extensible markup language (XML) has unique characteristics of information representation and exchange. In this chapter, the authors first couple the grey dynamic model with the spatiotemporal XML model. Based on a definition of the position part of the spatiotemporal XML model, they extract the corresponding position information of each time interval and propose an algorithm for constructing an AVL tree to store them. Then, the authors present the architecture of an interpolating and predicting process and investigate change operations in positions. On this basis, the chapter presents an algorithm for interpolation and prediction of spatiotemporal data based on XML integrated with the grey dynamic model.
Top1 Introduction
The management of spatiotemporal data has been intensively studied in recent years on account of the global spatiotemporal data increase (Blekas et al., 2008; Jadidi et al., 2014; Hallot & Billen, 2016). Among various applications (e.g., flight control, land management, blood forecasting, traffic supervision), the objects whose positions change over time, such as airplanes, ships, clouds, and so on, significantly contribute to the study of spatiotemporal data (Nalica, 2010; Aburas et al., 2017; Yu, 2006).
Researchers have addressed a variety of approaches in modeling and querying the location of the moving spatiotemporal data both in relational databases (Dobra et al., 2004; Güting et al., 2010; Alamri et al., 2014; Wu et al., 2001) and spatiotemporal databases (Choi & Chung, 2002; Hadjieleftheriou et al., 2003; Tao et al., 2004). Early studies on the positions of spatiotemporal data assumed that the spatiotemporal data remain static at each time interval (Langran, 1989; Seqev & Shoshani, 1987). In addition, representing and querying the locations of moving spatiotemporal data as a linear function of time, which assigns a set of possible values rather than a unique value, was introduced (Sistla et al., 1997). With respect to continuously moving regions, Tøssebro and Güting (Tøssebro & Güting, 2001) provided algorithms to interpolate between two snapshots, transitioning from simple convex polygons to arbitrary polygons. Güting et al. (Güting et al., 2010) identified the k nearest neighbors of a set of moving object trajectories for any instant of time within the lifetime. However, the fixed value model and linear model generate too many errors on account of the limited sampling rate and indeterminacy of the snapshot when increasing indeterminacy is concerned (Bao & Qin, 2005). Furthermore, the linear prediction models have limits (Sun et al., 2004). For example, the movement is not always linear; if it is linear, it is not usually known. If it is linear and known, it changes so fast that prediction using the movement parameter information is meaningless. Moreover, many spatiotemporal applications not only query the historical spatiotemporal data, but also strive to retrieve the near-future evolution of spatiotemporal data. Although the fixed value model and moving objects spatiotemporal (MOST) model (Wolfson, 1998) can provide the near-future evolution of spatiotemporal data, they assume the movement functions are already known. Additionally, when information is limited, the originally sampled data will generate errors. Therefore, a new modeling technology of interpolation and prediction of spatiotemporal data is required to overcome these problems.
In recent years, the grey system theory (Kayacan et al., 2010) has developed rapidly, and its applications have extended to the spatiotemporal data field. The grey prediction model, GM (1, 1), is used to enhance the estimation performance because it can reduce the randomness inside the history query results sequence and it generates a holistic measure. The grey system theory is fairly appropriate for predicting spatiotemporal information because the accumulated generating operation is the most important characteristic of the grey system theory. Its purpose is to reduce the randomness of data.