2013

paper

english

EN Collocation Pattern Discovery is a very interesting field of data mining in spatial databases. It consists in searching for types of spatial objects that are frequently located together in a spatial neighborhood. Application domains of such patterns include, but are not limited to, biology, geography, marketing and meteorology. To cope with processing of these huge volumes of data programmable high-performance graphic cards (GPU) can be used. GPUs have been proven recently to be extremely efficient in accelerating many existing algorithms. In this paper we present GPU-CM, a GPU-accelerated version of iCPI-tree based algorithm for the collocation discovery problem. To achieve the best performance we introduce specially designed structures and processing methods for the best utilization of the SIMD execution model. In experimental evaluation we compare our GPU implementation with a parallel implementation of iCPI-tree method for CPU. Collected results show order of magnitude speedups over the CPU version of the algorithm.

302 - 315

10.1007/978-3-642-40683-6_23

https://link.springer.com/chapter/10.1007/978-3-642-40683-6_23

Advances in databases and information systems : 17th East European Conference, ADBIS 2013, Genoa, Italy, September 1-4, 2013 : proceedings

17th East European Conference on Advances in Databases and Information Systems, ADBIS 2013, 1-4.09.2013, Genoa, Italy