Computational geometry: an introduction
Computational geometry: an introduction
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
A comparison of spatial query processing techniques for native and parameter spaces
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Join processing in relational databases
ACM Computing Surveys (CSUR)
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Hashing by proximity to process duplicates in spatial databases
CIKM '94 Proceedings of the third 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
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
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Non-Blocking Parallel Spatial Join Algorithm
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
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
ACM Transactions on Database Systems (TODS)
Identifying, tabulating, and analyzing contacts between branched neuron morphologies
IBM Journal of Research and Development
Scalable parallel minimum spanning forest computation
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
GIPSY: joining spatial datasets with contrasting density
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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Efficient spatial joins are pivotal for many applications and particularly important for geographical information systems or for the simulation sciences where scientists work with spatial models. Past research has primarily focused on disk-based spatial joins; efficient in-memory approaches, however, are important for two reasons: a) main memory has grown so large that many datasets fit in it and b) the in-memory join is a very time-consuming part of all disk-based spatial joins. In this paper we develop TOUCH, a novel in-memory spatial join algorithm that uses hierarchical data-oriented space partitioning, thereby keeping both its memory footprint and the number of comparisons low. Our results show that TOUCH outperforms known in-memory spatial-join algorithms as well as in-memory implementations of disk-based join approaches. In particular, it has a one order of magnitude advantage over the memory-demanding state of the art in terms of number of comparisons (i.e., pairwise object comparisons), as well as execution time, while it is two orders of magnitude faster when compared to approaches with a similar memory footprint. Furthermore, TOUCH is more scalable than competing approaches as data density grows.