Abstract
In spatial databases collocation pattern discovery is one of the most interesting fields of data mining. It consists in searching for types of spatial objects that are frequently located together in a spatial neighborhood. With the advent of data gathering techniques, huge volumes of spatial data are being collected. To cope with processing of such datasets a GPU accelerated version of the collocation pattern mining algorithm has been proposed recently [3]. However, the method assumes that a supporting structure that contains information about neighborhoods (called iCPI-tree) is given in advance. In this paper we present a GPU-based version of iCPI-tree generation algorithm for the collocation pattern discovery problem. In an experimental evaluation we compare our GPU implementation with a parallel implementation of iCPI-tree generation method for CPU. Collected results show that proposed solution is multiple times faster than the CPU version of the algorithm.
This work was partially supported from the Polish National Science Center (NCN), grant No. 2011/01/B/ST6/05169.
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Andrzejewski, W., Boinski, P. (2014). A Parallel Algorithm for Building iCPI-trees. In: Manolopoulos, Y., Trajcevski, G., Kon-Popovska, M. (eds) Advances in Databases and Information Systems. ADBIS 2014. Lecture Notes in Computer Science, vol 8716. Springer, Cham. https://doi.org/10.1007/978-3-319-10933-6_21
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DOI: https://doi.org/10.1007/978-3-319-10933-6_21
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