Multidimensional access methods
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Priority R-tree: a practically efficient and worst-case optimal R-tree
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Efficient query processing on unstructured tetrahedral meshes
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Accelerating Range Queries for Brain Simulations
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
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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.