A Novel Indexing Method for Spatial-Keyword Range Queries

Spatial-keyword queries are important for a wide range of applications that retrieve data based on a combination of keyword search and spatial constraints. However, efficient processing of spatial-keyword queries is not a trivial task because the combination of textual and spatial data results in a high-dimensional representation that is challenging to index effectively. To address this problem, in this paper, we propose a novel indexing scheme for efficient support of spatial-keyword range queries. At the heart of our approach lies a carefully-designed mapping of spatio-textual data to a two-dimensional (2D) space that produces compact partitions of spatio-textual data. In turn, the mapped 2D data can be indexed effectively by traditional spatial data structures, such as an R-tree. We propose bounds, theoretically proven for correctness, that lead to the design of a filter-and-refine algorithm that prunes the search space effectively. In this way, our approach for spatial-keyword range queries is readily applicable to any database system that provides spatial support. In our experimental evaluation, we demonstrate how our algorithm can be implemented over PostgreSQL and exploit its underlying spatial index provided by PostGIS, in order to process spatial-keyword range queries efficiently. Moreover, we show that our solution outperforms different competitor approaches.