Abstract
In this paper we introduce the notion of constrained nearest neighbor queries (CNN) and propose a series of methods to answer them. This class of queries can be thought of as nearest neighbor queries with range constraints. Although both nearest neighbor and range queries have been analyzed extensively in previous literature, the implications of constrained nearest neighbor queries have not been discussed. Due to their versatility, CNN queries are suitable to a wide range of applications from GIS systems to reverse nearest neighbor queries and multimedia applications. We develop methods for answering CNN queries with different properties and advantages. We prove the optimality (with respect to I/O cost) of one of the techniques proposed in this paper. The superiority of the proposed technique is shown by a performance analysis.
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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 work was partially supported by NSF grant EIA98-18320, IIS98-17432 and IIS99-70700.
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Ferhatosmanoglu, H., Stanoi, I., Agrawal, D., El Abbadi, A. (2001). Constrained Nearest Neighbor Queries. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds) Advances in Spatial and Temporal Databases. SSTD 2001. Lecture Notes in Computer Science, vol 2121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47724-1_14
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