MAMBO - Indexing Dead Space to Accelerate Spatial Queries✱

Abstract

With the increasing size and prevalence of spatial data across applications, efficiently indexing it becomes key. Minimum bounding boxes (MBBs) — i.e., axis-aligned rectangles that minimally enclose an object — used as approximations for complex geometric objects have become crucial for spatial indexes. MBBs succinctly summarize complex spatial objects and thus allow for an efficient filtering stage thanks to faster intersection tests. However, they introduce dead-space, i.e., space that is indexed but contains no spatial objects. Querying dead space gives no result but reads data from disk thus slowing down query execution unnecessarily. In this paper, we propose MaMBo (Meshed MBb), a grid-based data structure to index dead space in addition to an index of the spatial objects. We augment intersection operations of established indexes to consult our data structure while executing queries, thereby avoiding retrieval of unnecessary data from disk, i.e., data which only contains dead space. As our experiments show, we can significantly reduce I/O — the major overhead for disk-resident datasets — by over 50% when using MaMBo with an R-Tree.