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Compressing Relations And Indexes

Compressing Relations And Indexes. Jonathan Goldstein Raghu Ramakrishnan Uri Shaft Department of Compter Sciences, University of Wisconsin-Madison June 18, 1997. Agenda. Introduction Compressing A Relation Compression Applied to Rectangle Base Indexes Performance Evaluation

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Compressing Relations And Indexes

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  1. Compressing Relations And Indexes Jonathan Goldstein Raghu Ramakrishnan Uri Shaft Department of Compter Sciences, University of Wisconsin-Madison June 18, 1997

  2. Agenda Introduction Compressing A Relation Compression Applied to Rectangle Base Indexes Performance Evaluation Questions and Remarks

  3. Introduction • Page level Compression • Performance Study • Application to B-trees and R-trees • Multidimensional bulk loading algorithm

  4. Introduction

  5. Introduction

  6. Compressing A relation • Frames Of Reference • Non numeric attributes • File level compression

  7. Frames of Reference

  8. Lossy Compression Point approximation in lossy compression

  9. Compressing an indexing structure • Compressing a B-tree • Compressing a rectangle based indexing structure • Compression oriented Bulk Loading

  10. Rectangle Based indexing qualities

  11. Changing the frame of reference

  12. Bulk-Loading Algorithm • Input. A set of points in some n-dimentional space. • Output. A partition of the inut into subsets. • Requirements. The partition shuold group points that are close to each other in the same group as much as possiblg

  13. GB-Pack compression oriented bulk loading

  14. GB-Pack compression oriented bulk loading • Qualities: • trading off some tree quality for increased compression. • number of entries per page is data-dependent. • cutting a dimension in a value boundary in the data.

  15. GB-Pack compression oriented bulk loading

  16. GB-Pack compression oriented bulk loading

  17. GB-Pack compression oriented bulk loading

  18. Performance Evaluation • Relational Compression Experiments. • CPU vs. I/O Costs. • Comparison With Techniques in commercial systems. • Importance of Tuple-Level Decompression. • R-tree Compression Experiments.

  19. Synthetic Data Sets • Size: The number of tuples in the relation. • Dimensionality: The number of attributes of the relations. • Range: The range of values for the attributes. • Distribution :uniform(worst case) / exponential. • Partition Strategy. • Page size.

  20. Sales Data Set Sales data set. Compression Achieved versus dimensionality

  21. CPU vs. I/O Costs

  22. R-tree Compression Experiments Testing the quality of R-trees on Sales Data Set.

  23. Questions And Remarks

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