Query Relaxation and Result Ranking for Uncertain Spatiotemporal XML Data

Query Relaxation and Result Ranking for Uncertain Spatiotemporal XML Data

Luyi Bai, Jinyao Wang, Chengyu Zhang, Xiangfu Meng
Copyright: © 2022 |Pages: 19
DOI: 10.4018/JDM.313970
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Abstract

Due to the widespread uses of uncertain spatiotemporal data, web ordinary users have access to query these data in various ways. However, users often cannot accurately give query constraints so that the query results may be empty or very few. Traditional algorithms cannot be used to deal with uncertain spatiotemporal data because they have no relaxation query on spatiotemporal attributes. Therefore, in this paper, the authors propose new flexible query algorithms, which add relaxation query processing for spatiotemporal attributes. Considering that XML has great advantages in exchanging and representing spatiotemporal data, they propose an uncertain spatiotemporal data model based on XML. According to the different number of relaxing attributes, they give SingleRelaxation algorithm and MultipleRelaxation algorithm. In addition, a T-List structure is designed to quickly locate the nodes' positions of uncertain spatiotemporal data, and RSort algorithm is proposed to sort accurate query results and extended query results. The experimental results show the superiority of the approach.
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1. Introduction

In real world applications, the uses of spatiotemporal data are very extensive, such as traffic monitoring (Chen et al., 2003), large urban network (Laharotte et al., 2014), and person recognition (Gheissari et al., 2006), etc. Furthermore, spatiotemporal data have both spatial attributes and temporal attributes, and may change with time or space leading to uncertainties (Emrich et al., 2012). There are a lot of efforts studying temporal data modeling (Ma et al., 2022; Hu et al., 2015), spatiotemporal data modeling (Ma et al., 2013), uncertain spatiotemporal data modeling (Bai, et al., 2018), uncertain spatiotemporal reasoning, (Bai, et al., 2018), uncertain spatiotemporal querying (Emrich et al., 2012), fuzzy spatiotemporal data constructing, (Cheng, et al., 2019), fuzzy spatiotemporal modeling (Bai, et al. 2021), fuzzy spatiotemporal data representing (Cheng, et al. 2019), and fuzzy spatiotemporal data reasoning (Cheng, et al. 2021)

Owing to the development and popularization of science and technology, a large number of Web ordinary users can access information through various ways through spatiotemporal queries. Considering spatiotemporal query methods, Navathe et al. (Navathe & Ahmed, 1989) and Gao et al. (Gao et al., 2018) use temporal data query method, which queries whether the object changes within a certain period and the type of changes that have occurred. Dai et al. (Dai et al., 2005) and Sistla et al. (Sistla et al., 2015) apply the uncertain spatial data query, which queries the moving range of an object. Emrich et al. (Emrich et al., 2012) and Raghebi et al. (Raghebi & Banaei-Kashani, 2018) use the uncertain spatiotemporal data query technology, which queries the changes in a certain area within a certain time period. Wang et al. (Wang et al., 2013) utilize the join query method, which queries objects within a particular interval of time at a certain distance from a specific object. Furthermore, Wang et al. (Wang et al., 2013) use the spatiotemporal nearest neighbor query method, which queries an entity closest to the object or region under study within a specific time interval.

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