User-Contributed Relevance and Nearest Neighbor Queries

Novel Web technologies and resulting applications have lead to a participatory data ecosystem that when utilized properly will lead to more rewarding services. In this work, we investigate the case of Location-based Services and specifically of how to improve the typical location-based Point-Of-Interest POI request processed as a k-Nearest-Neighbor query. This work introduces Links-of-interest LOI between POIs as a means to increase the relevance and overall result quality of such queries. By analyzing user-contributed content in the form of travel blogs, we establish the overall popularity of a LOI, i.e., how frequently the respective POI pair is mentioned in the same context. Our contribution is a query processing method for so-called k-Relevant Nearest Neighbor k-RNN queries that considers spatial proximity in combination with LOI information to retrieve close-by and relevant as judged by the crowd POIs. Our method is based on intelligently combining indices for spatial data a spatial grid and for relevance data a graph during query processing. An experimental evaluation using real and synthetic data establishes that our approach efficiently solves the k-RNN problem when compared to existing methods.