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Highly Scalable Multiprocessing Algorithms for Preference-Based Database Retrieval

  • Joachim Selke
  • Christoph Lofi
  • Wolf-Tilo Balke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)

Abstract

Until recently algorithms continuously gained free performance improvements due to ever increasing processor speeds. Unfortunately, this development has reached its limit. Nowadays, new generations of CPUs focus on increasing the number of processing cores instead of simply increasing the performance of a single core. Thus, sequential algorithms will be excluded from future technological advances. Instead, highly scalable parallel algorithms are needed to fully tap new hardware potentials. In this paper we establish a design space for parallel algorithms in the domain of personalized database retrieval, taking skyline algorithms as a representative example. We will investigate the spectrum of base operations of different retrieval algorithms and various parallelization techniques to develop a set of highly scalable and high-performing skyline algorithms for different retrieval scenarios. Finally, we extensively evaluate these algorithms to showcase their superior characteristics.

Keywords

Design Space Shared Memory Main Memory Retrieval Algorithm Skyline Query 
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 2010

Authors and Affiliations

  • Joachim Selke
    • 1
  • Christoph Lofi
    • 1
  • Wolf-Tilo Balke
    • 1
  1. 1.Institut für InformationssystemeTechnische Universität BraunschweigBraunschweigGermany

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