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Benchmarking attribute cardinality maps for database systems using the TPC-D specifications.

B J Oommen1, M Thiyagarajah

  • 1Sch. of Comput. Sci., Carleton Univ., Ottawa, Ont., Canada.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
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New attribute cardinality maps (ACMs) significantly improve query result size estimation accuracy in database systems. Benchmarking demonstrates Rectangular-ACM and Trapezoidal-ACM outperform traditional histograms for query optimization.

Area of Science:

  • Database Systems
  • Query Optimization
  • Data Distribution Estimation

Background:

  • Benchmarking new software techniques is crucial for validating theories in specific domains.
  • Database system benchmarking is complex due to varying query types and settings.
  • Histogram-based techniques are common for query result size estimation but prone to errors.

Purpose of the Study:

  • To benchmark the performance of novel Rectangular-ACM (R-ACM) and Trapezoidal-ACM (T-ACM) techniques.
  • To evaluate the accuracy of ACMs for query result size estimation in database systems.
  • To demonstrate effective benchmarking of ACMs using TPC-D and synthetic datasets.

Main Methods:

  • Proposed and analyzed Rectangular-ACM and Trapezoidal-ACM, histogram-like techniques.

Related Experiment Videos

  • Conducted extensive experiments using TPC-D benchmark queries and database.
  • Utilized a large synthetic database with diverse query patterns for further validation.

    • Main Results:

    • R-ACM and T-ACM demonstrated significantly smaller estimation errors compared to equi-width and equi-depth histograms.
    • The proposed ACM schemes show superior accuracy in query result size estimation.
    • Successful benchmarking of ACMs was achieved using both standard and synthetic datasets.

    Conclusions:

    • Rectangular-ACM and Trapezoidal-ACM offer substantial improvements in query optimization accuracy.
    • These novel ACM techniques are more effective than traditional histograms for database systems.
    • The study validates the utility of ACMs and demonstrates a robust benchmarking methodology.