Verification of Partitioning and Allocation Techniques on Teradata DBMS

  • Ladjel Bellatreche
  • Soumia Benkrid
  • Ahmad Ghazal
  • Alain Crolotte
  • Alfredo Cuzzocrea
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7016)

Abstract

Data fragmentation and allocation in distributed and parallel Database Management Systems (DBMS) have been extensively studied in the past. Previous work tackled these two problems separately even though they are dependent on each other. We recently developed a combined algorithm that handles the dependency issue between fragmentation and allocation. A novel genetic solution was developed for this problem. The main issue of this solution and previous solutions is the lack of real life verifications of these models. This paper addresses this gap by verifying the effectiveness of our previous genetic solution on the Teradata DBMS. Teradata is a shared nothing DBMS with proven scalability and robustness in real life user environments as big as 10’s of petabytes of relational data. Experiments are conducted for the genetic solution and previous work using the SSB benchmark (TPC-H like) on a Teradata appliance running TD 13.10. Results show that the genetic solution is faster than previous work by a 38%.

Keywords

Data Warehouse Dimension Table Fact Table Allocation Technique Data Allocation 
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

  • Ladjel Bellatreche
    • 1
  • Soumia Benkrid
    • 2
  • Ahmad Ghazal
    • 3
  • Alain Crolotte
    • 3
  • Alfredo Cuzzocrea
    • 4
  1. 1.LISI/ENSMA Poitiers UniversityFuturoscopeFrance
  2. 2.National High School for Computer Science (ESI)AlgiersAlgeria
  3. 3.Teradata CorporationSan DiegoU.S.A.
  4. 4.ICAR-CNR and University of CalabriaItaly

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