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Bitmap Index Design and Evaluation

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Title: Bitmap Index Design and Evaluation


1
Bitmap Index Design and Evaluation
By Chee-Yong Chan Yannis E.Ioannidis
Ariel Noy Data representation and retrieval
seminar
2
Introduction
  • Query performance issues
  • On Line Transaction Processing.
  • Read write database.
  • Decision Support System.
  • Read mostly environments, with high selectivity
    factor.

3
Bitmap In Simple Form
Value List Index
  • Every value has its own column bitmap.

4
Advantages
  • Compact size.
  • Efficient hardware support for bitmap operations
    (AND, OR, XOR, NOT).
  • Fast search.
  • Multiple differentiate bitmap indexes for
    different kind of queries.

5
Selection queries.
  • Queries of the form A op v
  • A refers to indexed attribute.
  • Op
  • Range predicates
  • Equality predicates

6
Space time tradeoff of bitmap indexes, for
selection queries.
  • Space optimal bitmap index.
  • Time optimal bitmap index under a given space
    constraint.
  • Bitmap index with
  • optimal space
  • time tradeoff.
  • Time optimal bitmap
  • index.

7
Attribute Value Decomposition.
8
Bitmap Encoding Scheme
  • Equality Encoding
  • bi bits one for each possible value, all 0, vi 1.
  • Range Encoding
  • vi right most bits 0, rest 1.

9
Evaluation Algorithm for Range-Encoded Bitmap
Indexes.
  • RangeEval - ONeil and Quass
  • RangeEval-Opt
  • number bitmap operation 50 off
  • less bitmap scans for range predicate evaluation
  • caluclating only the requested bitmap
  • avoids the intermediate equality predicate
    evaluation by evaluating each range query in term
    only off lt based on
  • A lt v Altv-1
  • A gt v ! (Altv)
  • Agtv Altv-1
  • Working with only one bitmap B vs. working with
    at least two Beq and ( Blt or Bge)

10
Example
  • Alt864 using a 3 component base-10 index.
  • RaneEval-Opt
  • 4 operation 5 scans
  • RangeEval
  • 10 operations 6 scans

11
(No Transcript)
12
Analytical Comparison
13
Cost Model for Space-Time Tradeoff Analysis
  • Space(I)
  • Space metric is in term of number of bitmaps
    stored.
  • Time(I)
  • Time metric is in term of expected number of
    bitmap scans for a selection query evaluation.

14
Comparison of Bitmap Encoding Scheme
  • Equality encoded
  • S(I) C
  • T(I) nb/2
  • Range encoded
  • S(I) C-n
  • T(I) 2n

15
  • Space Optimal
  • number of bitmap in n-component space optimal
    n(b-2)
  • b
  • space efficiency is non-decreasing function of
    the number of components.
  • The ultimate optimal is when nlog(C)
  • Time Optimal
  • the optimal base in n-component base is
  • lt2,2,2,,C/2Ngt
  • time efficiency is non-increasing function of the
    number of components.
  • The ultimate optimal is when n1

16
Optimal Space-Time Tradeoff (knee).
  • Based on experimental, guessing and guts filling.
  • 2 component index
  • The base of the most time-efficient 2-component
    space-optimal index is given by

17
Time Optimal Bitmap Index Under Space Constraint
18
Bitmap Index Storage Schems
  • Bitmap Level Storage (BS)
  • each bitmap his own file
  • Component Level Storage (CS)
  • each index component has its own file
  • Index Level Storage (IS)
  • all together in one file

19
Compression of each file
  • CS has the best Space(I) tradeoff after
    compression.
  • BS has the best Time(I) tradeoff after
    compression.
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