TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Strohm, Peter Tim
A1  - Wittwer, Steffen
A1  - Haberstroh, Alexander
A1  - Lauer, Tobias
ED  - Bassiliades, Nick
ED  - Ivanović, Mirjana
ED  - Kon-Popovska, Margita
ED  - Manolopoulos, Yannis
ED  - Palpanas, Themis
ED  - Trajcevski, Goce
ED  - Vakali, Athena
T1  - GPU-Accelerated Quantification Filters for Analytical Queries in Multidimensional Databases
T2  - New Trends in Database and Information Systems II
N2  - In online analytical processing (OLAP), filtering elements of a given dimensional attribute according to the value of a measure attribute is an essential operation, for example in top-k evaluation. Such filters can involve extremely large amounts of data to be processed, in particular when the filter condition includes “quantification” such as ANY or ALL, where large slices of an OLAP cube have to be computed and inspected. Due to the sparsity of OLAP cubes, the slices serving as input to the filter are usually sparse as well, presenting a challenge for GPU approaches which need to work with a limited amount of memory for holding intermediate results. Our CUDA solution involves a hashing scheme specifically designed for frequent and parallel updates, including several optimizations exploiting architectural features of Nvidia’s Fermi and Kepler GPUs.
KW  - Datenbank
KW  - Informationssystem
KW  - Hash Function
KW  - Hash Table
KW  - Filter Dimension
KW  - Target Path
KW  - Analytical Query
Y1  - 2015
SN  - 978-3-319-10518-5
SB  - 978-3-319-10518-5
SN  - 978-3-319-10517-8
SB  - 978-3-319-10517-8
N1  - Parts of the research described in this paper were presented by the authors at Nvidia’s GPU Technology Conference in San Jose, CA (USA) in March 2014.
VL  - 2
SP  - 229
EP  - 242
PB  - Springer
CY  - Cham
ET  - 1.
ER  -