Abstract

A vast amount of geo-referenced data is being generated by mobile devices and other sensors increasing the importance of spatio-textual analyses on such data. Due to the large volume of data, the use of indexes to speed up the queries that facilitate such analyses is imperative. Many disk resident indexes have been proposed for different types of spatial keyword queries, but their efficiency is harmed by their high I/O costs. In this work, we propose , the first spatio-textual index that uses compact data structures to reduce the size of the structure, hence facilitating its usage in main memory. Our experimental evaluation, shows that this approach needs half the space and is more than one order of magnitude faster than a disk resident state-of-the-art index. Also, we show that our approach is competitive even in a scenario where the disk resident data structure is warmed-up to fit in main memory.