U-ASK: A Unified Architecture for kNN Spatial-Keyword Queries Supporting Negative Keyword Predicates

Spatial keyword queries have been popular in the research community for over a decade due to the explosive growth in user-generated data and its prime applications in different domains. kNN queries make a major category of spatial keyword queries that is heavily studied. However, the expressiveness of existing kNN queries is limited in supporting negative keyword predicates, e.g., find tweets with keywords "Chipotle" but NOT "Chipotle sauce", which have prime applications. In addition, existing architectures suffer from a lack of generality for different types of kNN queries. This paper proposes U-ASK; a Unified Architecture for Spatial-Keyword query supporting negative keyword predicates. U-ASK includes an indexing framework named TEQ (Textual-Enhanced Quadtree) and a query processor POWER (Parallel bOttom-up search With incrEmental pRuning) that handle various forms of kNN spatial keyword queries with negative keyword predicates. The experimental evaluation on real tweet datasets demonstrates up to 30x faster runtime compared to the state-of-the-art algorithms.