Definition
The spatial join is one of the core operators in spatial database systems. Efficient spatial join evaluation is important, due to its high cost compared to other queries, like spatial selections and nearest-neighbor searches. A binary (i.e., pairwise) spatial join combines two datasets with respect to a spatial predicate (usually overlap/intersect). A typical example is “find all pairs of cities and rivers that intersect.” For instance, in Fig. 1 the result of the join between the set of cities {c 1, c 2, c 3, c 4, c 5} and rivers {r 1, r 2}, is {(r 1, c 1), (r 2, c 2), (r 2, c 5)}.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Recommended Reading
Arge L, Procopiuc O, Ramaswamy S, Suel T, Vitter JS. Scalable sweeping-based spatial join. In: Proceedings of 24th International Conference on Very Large Data Bases; 1998. p. 570–81.
Brinkhoff T, Kriegel H-P, Seeger B. Efficient processing of spatial joins using r-trees. In: Proceedings of ACM SIGMOD International Conference on Management of Data; 1993. p. 237–46.
Corral A, Manolopoulos Y, Theodoridis Y, Vassilakopoulos M. Closest pair queries in spatial databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data; 2000. p. 189–200.
Güting RH. An introduction to spatial database systems. VLDB J. 1994;3(4):357–99.
Guttman A. R-trees: a dynamic index structure for spatial searching. In: Proceedings of ACM SIGMOD International Conference on Management of Data; 1984. p. 47–57.
Koudas N, Sevcik KC. Size separation spatial join. In: Proceedings of ACM SIGMOD International Conference on Management of Data; 1997. p. 324–35.
Koudas N, Sevcik KC. High dimensional similarity joins: algorithms and performance evaluation. IEEE Trans Knowl Data Eng. 2000;12(1):3–18.
Leutenegger ST, Edgington JM, Lopez MA. Str: a simple and efficient algorithm for R-tree packing. In: Proceedings of 13th International Conference on Data Engineering; 1997. p. 497–506.
Lo M-L, Ravishankar CV. Spatial hash-joins. In: Proceedings of ACM SIGMOD International Conference on Management of Data; 1996. p. 247–58.
Lo M-L, Ravishankar CV. The design and implementation of seeded trees: an efficient method for spatial joins. IEEE Trans Knowl Data Eng. 1998;10(1):136–52.
Mamoulis N, Papadias D. Slot index spatial join. IEEE Trans Knowl Data Eng. 2003;15(1):211–31.
Orenstein JA. Spatial query processing in an object-oriented database system. In: Proceedings of ACM SIGMOD International Conference on Management of Data; 1986. p. 326–36.
Papadopoulos A, Rigaux P, Scholl M. A performance evaluation of spatial join processing strategies. In: Proceedings of 6th International Symposium on Advances in Spatial Databases; 1999. p. 286–307.
Patel JM, DeWitt DJ. Partition based spatial-merge join. In: Proceedings of ACM SIGMOD International Conference on Management of Data; 1996. p. 259–70.
Preparata FP, Shamos MI. Computational geometry – an introduction. Springer; 1985.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Mamoulis, N. (2014). Spatial Join. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_356-2
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7993-3_356-2
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Online ISBN: 978-1-4899-7993-3
eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering