S2R-tree: a pivot-based indexing structure for semantic-aware spatial keyword search
S2R-tree: a pivot-based indexing structure for semantic-aware spatial keyword search
复制标题
S2R-tree：用于语义感知空间关键字搜索的基于枢轴的索引结构
DOI：
10.1007/s10707-019-00372-z
复制
发表时间：
2020-01-01
期刊：
影响因子：
2
通讯作者：
Zhao, Lei
中科院分区：
文献类型：
--
作者：
Chen, Xinyu;Xu, Jiajie;Zhao, Lei
语义感知的空间关键词搜索是数字地图服务的重要技术。然而，由于语义空间的高维性，现有的索引和搜索方法的剪枝效果有限，导致查询效率成为一个严重的问题。为了解决这个问题，本文提出了一种新的层次索引结构（SR）-R-2-树，以无缝的方式集成空间和语义信息。我们不需要在原始的语义空间中索引对象，而是精心设计了一种空间机制，将高维的语义向量转换到低维的空间中，从而达到更有效的剪枝效果。在（SR）-R-2-树的基础上，进一步设计了一个高效的查询处理算法，该算法不仅通过一组理论上的边界来保证高效的查询处理，而且即使在低维空间进行索引，也能返回准确的结果。此外，我们进行了广泛的实验，以评估和比较我们提出的方法和基线。
Semantic-aware spatial keyword search is an important technique for digital map services. However, existing indexing and search methods have limited pruning effect due to the high dimensionality in semantic space, causing query efficiency to be a serious issue. To handle this problem, this paper proposes a novel pivot-based hierarchical indexing structure (SR)-R-2-tree to integrate spatial and semantic information in a seamless way. Instead of indexing objects in the original semantic space, we carefully design a space mechanism to transform the high dimensional semantic vectors to a low dimensional space, so that more effective pruning effect can be achieved. On top of the (SR)-R-2-tree, an efficient query processing algorithm is further designed, which not only ensures efficient query processing by a set of theoretical bounds, but also returns accurate results despite of the indexing in the low dimensional space. Furthermore, we conduct extensive experiments to evaluate and compare our proposed and baseline methods.