Spatial textual Top-k search in mobile peer-to-peer networks

Mobile hardware and software is quickly becoming the dominant computing model for technologically savvy people around the world. Nowadays, mobile devices are commonly equipped with GPS and wireless connections. Users have also developed the habit of regularly checking into a location, and adding comments or ratings for restaurants or any place of interest visited. This work explores new approaches to make data available from a local network, and to build a collaborative search application that can suggest locations of interest based on distance, user reviews and ratings. The proposed system includes light-weight indexing to support distributed search over spatio-textual data on mobile devices, and a ranking function to score objects of interest with relevant user review content. From our experimental study using a Yelp dataset, we found that our proposed system provides substantial efficiency gains when compared with a centralised system, with little loss in overall effectiveness. We also present a methodology to quantify efficiency and effectiveness trade-offs in decentralized search systems using the Rank-based overlap (RBO) measure.