LAUSR

Current spatial-keyword publish/subscribe systems need to handle spatial-keyword skyline queries over geo-textual streams to continuously obtain good results. The skyline queries in such systems face two main problems: (1) query problems, because the powerful query capability is required for the strict limit of the response time and the large number of items concerned by the users, and (2) scalability issue, because millions of active users are maintained simultaneously with many network-connected machines. Unfortunately, the current approach is towards static data. Thus, this paper first proposes a distributed skyline query processing framework. Then, we optimize the skyline computing by introducing MF-R$^t$t-tree, which is an update-efficient and space-saving indexing structure and a fast approach for processing a continuous spatial-keyword skyline query called $eager^*$eager*. Finally, a spatial and textual signature-based communication optimization method is proposed to support scalability. The experimental results indicate that (1) MF-R$^t$t-tree can significantly reduce update costs, while maintaining a low storage cost, and a query performance comparable to IL-Quadtree, (2) $eager^*$eager* can averagely accelerate 79.72 × faster than the method based on BNL, (3) the communication optimization method significantly reduces the communication cost, and (4) the distributed framework can efficiently support large-scale skyline queries.