Joint Top-K Spatial Keyword Query Processing

Abstract

Abstract-Web users and content are increasingly being geo-positioned, and increased focus is being given to serving local content in response to web queries. This development calls for spatial keyword queries that take into account both the locations and textual descriptions of content. We study the efficient, joint processing of multiple top-k spatial keyword queries. Such joint processing is attractive during high query loads and also occurs when multiple queries are used to obfuscate a user's true query. We propose a novel algorithm and index structure for the joint processing of top-k spatial keyword queries. Empirical studies show that the proposed solution is efficient on real datasets. We also offer analytical studies on synthetic datasets to demonstrate the efficiency of the proposed solution.

Mercury: A memory-constrained spatio-temporal real-time search on microblogs

“…None of these work have addressed the case of locationaware search. On the other hand, spatial keyword search is well studied on web documents and web spatial objects [9], [10], [20], [41], [44]. However, they use offline disk-based data partitioning indexing, which cannot scale to support the dynamic nature and arrival rates of microblogs [5], [8].…”

On Generalizing Collective Spatial Keyword Queries

“…A boolean kNN query [12], [5], [24], [30], [27] finds a list of k objects each covering all specified query keywords. The objects in the list are ranked based on their spatial proximity to the query location.…”

An Efficient Algorithm for Location-Aware Query Autocompletion

“…This problem has been extensively studied in the database community. Existing solutions are based on Rtree [12]- [16], grid [17], [18], and space filling curve [19]. We also refer users to an experimental evaluation [20] that compares these methods.…”