It is increasingly common for domain scientists to use computational tools to build and simulate spatial models of the phenomena they are studying. The spatial models they build are more and more detailed as well as dense and are consequently difficult to manage with today's tools. A crucial problem when analyzing spatial models of increasing detail is the scalable execution of range queries. State-of-the-art approaches like the R-Tree perform suboptimally on today's models and do not scale for more dense, future models. The problem is that the amount of overlap in the tree structure increases as a function of the level of detail/density in the model.

In this demonstration we showcase ZOOM, a new tool to efficiently execute spatial range queries on increasingly detailed (denser) models. ZOOM is based on FLAT, a novel range query execution approach that effectively decouples the query execution time from the density of the dataset, thereby ensuring efficient query execution. At the core of the demonstration thus is the visualization of the novel query execution strategy of FLAT which we contrast with a visualization of the query execution of the R-Tree.