Efficient Group Top-k Spatial Keyword Query Processing

With the proliferation of geo-positioning and geo-tagging, spatial web objects that possess both a geographical location and textual description are gaining in prevalence. Given a spatial location and a set of keywords, a top-k spatial keyword query returns the k best spatio-textual objects ranked according to their proximity to the query location and relevance to the query keywords. To our knowledge, existing study on spatial keyword query processing only focuses on single query point scenario. In this paper, we take the first step to study the problem of multiple query points (or group queries) top-k spatial keyword query processing. We first propose a threshold-based algorithm, which first performs incremental top-k spatial keyword query for each query point and then combines their results. Next, we propose another more efficient algorithm by treating the whole query set as a query unit, which can significantly reduce the objects to be examined, and thus achieve higher performance. Extensive experiments using real datasets demonstrate that our approaches are efficient in terms of runtime and I/O cost, as compared to the baseline algorithm.