Real-Time Computation of Advanced Rules in OLAP Databases

  • Steffen Wittmer
  • Tobias Lauer
  • Amitava Datta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6909)


In Online Analytical Processing (OLAP) users view data through a multidimensional model known as the data cube, allowing the aggregation of information along different attributes and operations such as slicing and dicing. In-memory OLAP systems keep all relevant data in main memory and also support efficient updates of cube data, enabling interactive planning, forecasting, and what-if analysis. Since usually only the base data is stored and all aggregations and other calculations are computed on the fly, complex computations may seriously downgrade performance. We present an approach that uses graphics processing units (GPUs) as parallel coprocessors for high performance in-memory OLAP operations. In particular, our method accelerates the calculation of compute-intensive rules, which represent business dependencies that are more complex than mere aggregates. In addition to the data structures and algorithms, we describe how to extend the approach to multi-GPU systems in order to scale it to larger data sets.


Data Cube Fact Table Matching Fact Rule Operation Advance Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Steffen Wittmer
    • 1
  • Tobias Lauer
    • 2
  • Amitava Datta
    • 3
  1. 1.Jedox AGFreiburgGermany
  2. 2.University of FreiburgGermany
  3. 3.University of Western AustraliaPerthAustralia

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