一种针对反向空间偏好top-k查询的高效处理方法

随着地理位置定位技术的蓬勃发展，基于在线位置服务技术的应用也越来越多.提出一种查询类型——反向空间偏好top-k查询.类似于传统的反向空间top-k查询，对于给定的空间查询对象，该查询返回使该对象满足top-k属性得分的那些用户.但不同的是，该对象的属性不是自身具有的特性，而是通过计算该对象与其他偏好对象之间的空间关系（如距离）而确定.这种查询在市场分析等许多重要领域具有需求，例如，根据查询结果，分析出某个地区中某个设施受欢迎的程度.但是，由于大量空间对象的存在导致对象之间空间关系的计算代价非常高，如何实时地计算出对象的空间属性得分，给查询处理带来很大的挑战.针对该问题提出优化的查询处理算法包括：数据集剪枝、数据集批量处理、基于权重的用户分组等策略.通过理论分析和充分的实验验证，证明了所提出方法的有效性.与普通方法相比，这些方法能够大幅度提高查询处理的执行时间和I/O效率.

Efficient Processing Method for Reverse top-k Spatial Preference Queries

With the proliferation of geo-positioning techniques, there has been increasing popularity of online location-based services. Specifically, reverse top-k spatial preference queries provide such services to retrieve the users that deem a given database object as one of their top-k results. The attributes of the query object are given by the spatial distance from users'' preference. However in real world, users not only consider the non-spatial attributes about the objects, but also hope to find the spatial objects based on the qualities of features in their spatial neighborhood. While reverse top-k spatial preference queries have significant amount of real-life applications such as market analysis, for example, to predict the popularity of a facility in a region, they face a great challenge to compute the score of the spatial attributes online. This paper presents a processing framework and some optimal techniques including pruning and user preference grouping methods. Theoretical analysis and experimental evaluation demonstrate the efficiency of the proposed algorithms and the improvement on running time and I/O.