A Compact Memory-based Index for Spatial Keyword Query Resolution

Spatial keyword queries are massively used to provide innovative search services, such as retrieving the nearest restaurant offering a desired service. Behind these services, geo-textual indexes take a leading role in efficiently resolving such queries. Existing approaches combine spatial and text indexing schemes that are based primarily on secondary storage, so their performance is mainly affected by I/O costs. To overcome this limitation, a new compact memory-based index is proposed that enhances a balanced KD-Tree with keyword information encoded in the form of highly-compressed bitmaps. We also design an in-memory algorithm that efficiently resolves the Top-k Spatial Keyword Query; i.e. it retrieves the k nearest objects that are described by a set of keywords. The experiments run in this research, involving a real-world datasets, show that our propose overcome the state of the art both in space requirement (27 percent in comparison) and runtime (12.5 times faster).