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A Spatial Index is a data-structure designed to enable fast access to spatial data. Spatial data come in various forms, the most common being points, lines, and regions in n-dimensional space (practically, n = 2 or 3 in GIS Geographical Information System applications). Typical “selection” queries include the spatial range query (“find all objects that lie within a given query region”), and the spatial point query (“find all objects that contain a given query point”). In addition, multi-dimensional data introduce spatial relationships (such as overlapping and disjointness) and operators (e.g., nearest neighbor), which need to be efficiently supported as well. Example queries are the spatial join query (“find all pairs of objects that intersect each other”) and the nearest neighbor query (“find the five objects nearest to a given query point”). It should be noted that traditional indexing approaches (B+-trees, etc.) are not appropriate for...
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Beckmann N., Kriegel H.-P., Schneider R., and Seeger B. The R*-tree: an efficient and robust access method for points and rectangles. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1990, pp. 322–331.
Finkel R.A. and Bentley J.L. Quad Trees: a data structure for retrieval on composite keys. Acta Informatica, 4(1):1–9, 1974.
Freeston M.A. General solution of the n-dimensional B-tree problem. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1995, pp 80–91.
Gaede V. and Guenther O. Multidimensional access methods. ACM Comput. Surv., 30(2):170–231, 1998.
Guttman A. R-trees: a dynamic index structure for spatial searching. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1984, pp. 47–57.
Hadjieleftheriou M., Hoel E., and Tsotras V.J. SaIL: A spatial index library for efficient application integration. GeoInformatica, 9(4):367–389, 2005.
Henrich A., Six H.-W., and Widmayer P. The LSD tree: spatial access to multidimensional point and non point objects. In Proc. 15th Int. Conf. on Very Large Data Bases, 1989, pp. 43–53.
Kamel I. and Faloutsos C. Hilbert R-tree: an improved R-tree using fractals. In Proc. 20th Int. Conf. on Very Large Data Bases, 1994, pp. 500–509.
Lomet D.B. and Salzberg B. The hB-tree: a multiattribute indexing method with good guaranteed performance. ACM Trans. Database Syst., 15(4):625–658, 1990.
Nievergelt J., Hinterberger H., and Sevcik K.C. The grid file: an adaptable symmetric multikey file structure. ACM Trans. Database Syst., 9(1):38–71, 1984.
Roussopoulos N. and Leifker D. Direct Spatial Search on Pictorial Databases Using Packed R-trees. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1985, pp. 17–31.
Roussopoulos N., Kelley S., and Vincent F. Nearest neighbor queries. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1995, pp. 71–79.
Samet H. The Design and Analysis of Spatial Data Structures. Addison-Wesley, 1990.
Seeger B. and Kriegel H.-P. The Buddy-tree: an efficient and robust access method for spatial database systems. In Proc. 16th Int. Conf. on Very Large Data Bases, 1990, pp. 590–601.
Sellis T., Roussopoulos N., and Faloutsos C. The R+-tree: a dynamic index for multidimensional objects. In Proc. 13th Int. Conf. on Very Large Data Bases, 1987, pp. 507–518.
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Manolopoulos, Y., Theodoridis, Y., Tsotras, V.J. (2009). Spatial Indexing Techniques. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_355
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DOI: https://doi.org/10.1007/978-0-387-39940-9_355
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