The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Spatial joins using seeded trees
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
The Montage extensible DataBlade architecture
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Partition based spatial-merge join
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Cascaded spatial join algorithms with spatially sorted output
GIS '96 Proceedings of the 4th ACM international workshop on Advances in geographic information systems
A greedy algorithm for bulk loading R-trees
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
Fundamentals of Database Systems
Fundamentals of Database Systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Scalable Sweeping-Based Spatial Join
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Efficient query processing on unstructured tetrahedral meshes
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
ACM Transactions on Database Systems (TODS)
The priority R-tree: A practically efficient and worst-case optimal R-tree
ACM Transactions on Algorithms (TALG)
Identifying, tabulating, and analyzing contacts between branched neuron morphologies
IBM Journal of Research and Development
Accelerating Range Queries for Brain Simulations
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
TOUCH: in-memory spatial join by hierarchical data-oriented partitioning
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Many scientific and geographical applications rely on the efficient execution of spatial joins. Past research has produced several efficient spatial join approaches and while each of them can join two datasets, the problem of efficiently joining two datasets with contrasting density, i.e., with the same spatial extent but with a wildly different number of spatial elements, has so far been overlooked. State-of-the-art data-oriented spatial join approaches (e.g., based on the R-Tree) suffer from degraded performance due to overlap, whereas space-oriented approaches excessively read data from disk. In this paper we develop GIPSY, a novel approach for the spatial join of two datasets with contrasting density. GIPSY uses fine-grained data-oriented partitioning and thus only retrieves the data needed for the join. At the same time it avoids the overlap related problems associated with data-oriented partitioning by using a crawling approach, i.e., without using a hierarchical tree. Our experiments show that GIPSY outperforms state-of-the-art disk-based spatial join algorithms by a factor of 2 to 18 and is particularly efficient when joining a dense dataset with several sparse datasets.