Abstract
The ability to handle Big Data is one of the key requirements of today’s database systems. Calculating conditional business rules in OLAP scenarios means creating virtual cube cells out of previously stored database entries and precalculated aggregates based on a given condition. It requires passing several steps such as source data filtering, aggregation and conditional analysis, each involving storing intermediate results which can easily get very large. Therefore, algorithms allowing to stream data instead of calculating the results in one step are essential to process big sets of data without exceeding the hardware limitations. This paper shows how the evaluation of conditional business rules can be accelerated using GPUs and massively data-parallel streaming-algorithms written in CUDA.
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Haberstroh, A., Strohm, P. (2015). Big Data Conditional Business Rule Calculations in Multidimensional In-GPU-Memory OLAP Databases. In: Morzy, T., Valduriez, P., Bellatreche, L. (eds) New Trends in Databases and Information Systems. ADBIS 2015. Communications in Computer and Information Science, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-319-23201-0_32
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DOI: https://doi.org/10.1007/978-3-319-23201-0_32
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