Searching and Updating Metric Space Databases Using the Parallel EGNAT
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- Marin M., Uribe R., Barrientos R. (2007) Searching and Updating Metric Space Databases Using the Parallel EGNAT. In: Shi Y., van Albada G.D., Dongarra J., Sloot P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4487. Springer, Berlin, Heidelberg
The Evolutionary Geometric Near-neighbor Access Tree (EGNAT) is a recently proposed data structure that is suitable for indexing large collections of complex objects. It allows searching for similar objects represented in metric spaces. The sequential EGNAT has been shown to achieve good performance in high-dimensional metric spaces with properties (not found in others of the same kind) of allowing update operations and efficient use of secondary memory. Thus, for example, it is suitable for indexing large multimedia databases. However, comparing two objects during a search can be a very expensive operation in terms of running time. This paper shows that parallel computing upon clusters of PCs can be a practical solution for reducing running time costs. We describe alternative distributions for the EGNAT index and their respective parallel search/update algorithms and concurrency control mechanism.
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