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. 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