A presentation conducted by Dr Jun Shen, School of Information Systems and Technology University of Wollongong. Presented on Tuesday the 1st of October 2013 With the rapid proliferation of services and cloud computing, Big Data has become a significant phenomenon across many scientific disciplines and sectors of society, wherever huge amounts of data are generated and processed daily. End users will always seek higher-quality data access at lower prices. This demand poses challenges to service composers, service providers and data providers, who should maintain their service and data provision as cost-effectively as possible. This paper will apply bio inspired approaches to achieving equilibrium among the otherwise competitive stakeholders. In addition to novel models of cost for Big Data provision, bio-inspired algorithms will be developed and validated for dynamic optimisation. Furthermore, the optimised algorithms will also be applied in the data-mining research on the Alpha Magnetic Spectrometer (AMS) experiment, which is aiming to find dark matter in the universe. This experiment typically receives 200G and generates 700G data daily.

1. ENDORSING PARTNERS
Bio-inspired costeffective access to big
data
The following are confirmed contributors to the business and policy dialogue in Sydney:
•
Rick Sawers (National Australia Bank)
•
Nick Greiner (Chairman (Infrastructure NSW)
Monday, 30th September 2013: Business & policy Dialogue
3rd
www.isngi.org
Tuesday 1 October to Thursday,
October: Academic and Policy
Dialogue by: Dr Jun Shen, School of Information Systems and Technology
Presented
University of Wollongong,
www.isngi.org

2. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Bio-inspired cost-effective access to big data
Lijuan Wang
Jun Shen
School of Information Systems and Technology
University of Wollongong, Australia
lw840@uowmail.edu.au
ISNGI 2013
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

3. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Outline
Introduction
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

4. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
A few streams of big data
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

5. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Outline
Introduction
Problem statement
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

6. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Basic concepts
Services
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

7. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Basic concepts
Services
Abstract services
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

8. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Basic concepts
Services
Abstract services
Concrete services
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

9. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Basic concepts
Services
Abstract services
Concrete services
Quality of service (QoS)
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

10. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Basic concepts
Services
Abstract services
Concrete services
Quality of service (QoS)
Web service composition
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

11. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Service and data usage and charging relationship
Data Provider
pay
provide
request
charge
data set
Service Provider
request
pay
provide
elementary service
charge
Service Composer
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

12. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Optimizations in data-intensive service composition
optimisation point 1
optimisation point 2
concrete
services
data replicas
replica 1
csn,1
replica 2
csn,2
optimisation point 3
abstract
services
datasets
dataset 1
AS1
dataset 2
AS2
replica l-1
csn,m-1
replica l
csn,m
Application
dataset k-1
ASn
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
dataset k
University of Wollongong, Australia

13. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Outline
Introduction
Problem statement
Bio-inspired cost-effective to access big data
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

14. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Why bio-inspired algorithms
Global optimization approach
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

15. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Why bio-inspired algorithms
Global optimization approach
Less computation time
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

16. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Why bio-inspired algorithms
Global optimization approach
Less computation time
Features such as autonomy, scalability, adaptability and
robustness
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

17. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Bio-inspired algorithms
Biological systems are autonomous entities and
self-organized
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

18. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Bio-inspired algorithms
Biological systems are autonomous entities and
self-organized
Simplicity and rapid convergence
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

19. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Bio-inspired algorithms
Biological systems are autonomous entities and
self-organized
Simplicity and rapid convergence
Strengths in optimizing dynamic negotiations
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

20. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Case study: Alpha Magnetic Spectrometer (AMS)
Monte Carlo Simulation
Analog Detectors
Simulation Data
AMS-02
Package
CEANT3
Data Capture
AMS-02
Data reconstruction
Raw Data
Physical Analysis
Result Storage
Data reconstruction
ROOT
Query and Display
Correction Data
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
Visualization
University of Wollongong, Australia

21. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Outline
Introduction
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

22. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
GA and MIP
8000
Computation Time (msce)
7000
Genetic Algorithm
Mixed Integer Programming
6000
5000
4000
3000
2000
1000
0
10
20
30
40
50
Number of abstract services
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

23. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
GA and MIP
Computation Time (msec)
1400
1200
Genetic Algorithm
Mixed Integer Programming
1000
800
600
400
200
0
100
200
300
400
500
600
700
800
900
1000
Number of candidate services per class
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

24. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
ACS and GA
QWS
9000
Computation Time (msec)
8000
ACS
GA
7000
6000
5000
4000
3000
2000
1000
0
10
20
30
40
50
Number of abstract services
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

25. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
ACS and GA
QWS
9000
8000
ACS
GA
Computation time (msec)
7000
6000
5000
4000
3000
2000
1000
0
100
200
300
400
500
600
700
800
900
1000
Number of candidate services per class
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia

26. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
MOACS and MOGA
Median Summary Attainment Surface
5
5
x 10
MOACS:n30m50
MOGA:n30m50
Overall Execution Time
4.5
4
3.5
3
2.5
2
1.5
3.2
3.4
3.6
3.8
4
4.2
4.4
Overall Cost
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
4.6
4.8
5
5.2
4
x 10
University of Wollongong, Australia

27. Outlines
Problem statement
Bio-inspired cost-effective to access big data
Conclusion and future work
Summary
Thank you very much!
Questions and suggestions are welcome.
Lijuan Wang, Jun Shen
Bio-inspired cost-effective access to big data
University of Wollongong, Australia