一种基于样本的空间数据库数据质量评估方法及其在移动轨迹最近邻搜索中的应用
A Sample-Based Approach to Data Quality Assessment in Spatial Databases with Application to Mobile Trajectory Nearest-Neighbor Search
摘要：
空间数据在网络和移动制图以及地理信息系统等新技术中发挥着新兴作用。政治、社会和现代人类生活的许多其他方面的重要决策正在使用位置数据做出。许多国家的决策者正在利用空间数据库来收集信息、分析信息和规划未来。事实上，并不是所有的空间数据库都适合这种类型的应用。位置数据与任何其他类型的数据一样，存在不准确、不精确和其他缺陷，可能对基于未经提炼的信息采取的任何行动的可信度产生负面影响。因此，我们需要一种方法来评估空间数据的质量，并将有用的数据与导致弱决策的误导性数据分开。另一方面，空间数据库通常规模巨大，因此使用此类数据会对效率产生负面影响。为了提高使用空间大数据的效率，我们需要一种方法来缩小数据量。抽样是其中一种方法，但它对数据质量的负面影响是不可避免的。在本文中，我们试图显示和评估这种变化的空间数据的质量是抽样的结果。我们使用这种方法来评估在中国的移动用户轨迹相关的采样空间数据的质量，这是在一个著名的空间数据库。结果表明，基于样本的数据质量控制将显着提高查询性能，而不会失去太多的准确性。基于此结果，一些未来的改进指出，这将有助于处理基于位置的查询比以前更快，并在有限的时间内做出更准确的基于位置的决策。
英文摘要：
Spatial data is playing an emerging role in new technologies such as web and mobile mapping and Geographic Information Systems (GIS). Important decisions in political, social and many other aspects of modern human life are being made using location data. Decision makers in many countries are exploiting spatial databases for collecting information, analyzing them and planning for the future. In fact, not every spatial database is suitable for this type of application. Inaccuracy, imprecision and other deficiencies are present in location data just as any other type of data and may have a negative impact on credibility of any action taken based on unrefined information. So we need a method for evaluating the quality of spatial data and separating usable data from misleading data which leads to weak decisions. On the other hand, spatial databases are usually huge in size and therefore working with this type of data has a negative impact on efficiency. To improve the efficiency of working with spatial big data, we need a method for shrinking the volume of data. Sampling is one of these methods, but its negative effects on the quality of data are inevitable. In this paper we are trying to show and assess this change in quality of spatial data that is a consequence of sampling. We used this approach for evaluating the quality of sampled spatial data related to mobile user trajectories in China which are available in a well-known spatial database. The results show that sample-based control of data quality will increase the query performance significantly, without losing too much accuracy. Based on this results some future improvements are pointed out which will help to process location-based queries faster than before and to make more accurate location-based decisions in limited times.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
