1. 1
Data Compression for Large
Multidimensional Data
Warehouses
Supervisor: Presented by:
Dr. K.M. Azharul Hasan Abdullah Al Mahmud,
Associate Professor, Roll : 0507006
Head of the Department, Md. Mushfiqur Rahman,
Department of CSE, KUET Roll : 0507029
This slide is prepared by Muhammad Mushfiqur Rahman & Abdullah Al Mahmud for the presentation of Thesis

2. 2
Presentation Layout
 Objectives
 Existing Compression Schemes
 Traditional Extendible Array
 Proposed Compression Scheme
 EXCS
(Extendible Array Based Compression Scheme)
Comparative Analysis
Conclusion
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

3. 3
Objectives
Data compression technology reduces:
 effective price of logical data storage capacity
improves query performance
 Multidimensional array is widely used in large
number of scientific research.
 An efficient compression of multidimensional
array can handle large multidimensional data
sets of data warehouses
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

4. 4
Existing Compression Schemes (1/ 3)
 Bitmap compression
 Run Length Encoding
 Header compression
 Compressed Column Storage
 Compressed Row Storage
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

5. 5
Existing Compression Schemes (2/ 3)
(a) A sparse array. (b) The CRS scheme
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

6. 6
Existing Compression Schemes (3/ 3)
Classical methods cannot support updates
without completely readjusting runs .
Compressing sparse array
 Do not support extendibility
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

7. 7
Traditional Extendible Array
History
Table
0 1 3 5
 TEA supports
dynamic extension Address
Table
0 1 4 9
of dimension size.
0 0 0 1 4 9
Position <1,3> 2 2 2 3 5 10
H1[1]<H2[3] 4 6 6 7 8 11
Address of History Counter= 0
4
2
3
5
1
Cell=Address1[3]+1=10
Figure 1: TEA Construction And Access
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

8. 8
Proposed Compression Scheme
Multidimensional arrays are important for
sparse array operations
Extendibility of multidimensional arrays
 A compression technique that can work on
multidimensional extendible array
 Our proposed compression scheme is EXCS
(Extendible array based Compression
Scheme)
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

9. 9
Extendible array based
Compression Scheme (EXCS) 1/3
We implemented the multidimensional
extendible array in secondary memory
We have considered dimension =3 in our
experimental approach
The sub-arrays are distinguished to store
them individually in the secondary memory
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

10. 10
Extendible array based
Compression Scheme (EXCS) 2/3
The sub-arrays are of n-1(=2) dimension
A large no. of sub-arrays are generated to be
compressed
Sub-arrays are dynamically taken as input
Only the max no of sub-arrays is to be given
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

11. 11
Extendible array based
Compression Scheme (EXCS) 3/3
Each sub-array is compressed individually
The compression technique used is similar to
CRS
The compressed elements are written in the
secondary memory as RO, CO, VL of
subarray_1, subarray_2, … … subarray_N
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

12. 12
Performance Measurement
Performance is measured by measuring two
key factors of the compression schemes:
 Data Density
 Length of Dimension/ Number of Data
 compression ratio=
(compressed data/ original data)
 space savings = 1 – compression ratio
 we have considered space savings in percent
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

13. 13
Comparative Analysis (1/4)
100
80
60
Space savings
Header
40
Bitmap
CRS
EACRS
20
Offset
0
64 729 4096 15625 46656
-20
-40
No. of data
Figure: Comparison with fixed density = 20%
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

14. 14
Comparative Analysis (2/4)
80
60
40
Space savings
Header
Bitmap
20 CRS
EACRS
Offset
0
64 729 4096 15625 46656
-20
-40
No. of data
Figure: Comparison with fixed density = 25%
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

15. 15
Comparative Analysis (3/4)
100
80
60
compression ratio
40
Header
Bitmap
20
CRS
0
EACRS
10 20 30 40 50
Offset
-20
-40
-60
Density of data
Figure: Comparison with fixed no. of data=64
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

16. 16
Comparative Analysis 100
(4/4)
80
60
compression ratio
40
Header
Bitmap
20 CRS
EACRS
Offset
0
10 20 30 40 50
-20
-40
-60
Density of data
Figure: Comparison with fixed no. of data=4096
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

17. 17
Performance Measurement
 Extendibility of arrays
 Using multidimensional arrays
 Extendibility toward any dimension
EXCS allows dynamic extension of arrays.
In analysis, we can extend data up to n
dimensions
 Performance is good for large no. of data
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh

18. 18
Conclusion
 Our proposed compression scheme is
experimentally done up to 3 dimension data
 It can be extended experimentally for
compressing n dimension data in future.
EXCS is effective for large multidimensional
data warehouses
Muhammad Mushfiqur Rahman, Student ID: 0507029, CSE, KUET, Bangladesh