K-SPIN: efficiently processing spatial keyword queries on road networks (Extended Abstract)

Given the prevalence and volume of local search queries, today's search engines are required to find results by both spatial proximity and textual relevance at high query throughput. Existing techniques to answer such spatial keyword queries employ a keyword aggregation strategy that suffers from certain drawbacks when applied to road networks. Instead, we propose the K-SPIN framework, which uses an alternative keyword separation strategy that is more suitable on road networks. While this strategy was previously thought to entail prohibitive pre-processing costs, we further propose novel techniques to make our framework viable and even light-weight. Thorough experimentation shows that K-SPIN outperforms the state-of-the-art by up to two orders of magnitude on a wide range of settings and real-world datasets.