This presentation talking something about big data.Especially,big data application and challenge ahead.

1. Big Data
Group C: Wei Luo,JunHao Min,ZhiXiang Guo
JiaQiang Dong,Hui Huang
WHUT

2. What is Big Data
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we are a part of it every day

3. What Does Big Data Look Like

4. In Practice
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Cloud or in-house?
Big data is big
Big data is messy
Culture

5. appdomain of big data
Internet domain
 Weather domain
 Telecommunication domain
 Medical domain
 Demographics domain
 Financial domain
 Application Which use the technology of
big data benefit to us
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6. Automotive
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Data warehouse optimization
Predictive asset optimization
Connected vehicle
Actionable customer insight
Telecommunications
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active call center
Smarter campaigns
Network analytics
Location-based services

7. 
Banking
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Optimize offers and cross sell
Contact center efficiency and problem
resolution
Payment fraud detection and
investigation
Counterparty credit risk management
Insurance
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Create a customer-focused enterprise
Optimize enterprise risk management
Optimize multi-channel interaction
Increase flexibility and streamline
operations

8. 
Consumer Products
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Micro-market campaign management
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Optimized promotions effectiveness
Real-time demand forecast
Oil & Gas
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Advanced condition monitoring
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Drilling surveillance & optimization
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Production surveillance & optimization

9. 
Energy and Utilities
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Distribution load forecasting and scheduling
Create targeted customer offerings
Condition-based maintenance
Enable customer energy management
Smart meter analytics
Government
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Threat prediction and prevention
Social program fraud, waste and errors
Tax compliance - fraud and abuse
Crime prediction and prevention

10. 
Healthcare
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Measure and act on population health
Engage consumers in their healthcare
Health monitoring and intervention
Travel & Transportation
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Customer analytics and loyalty marketing
Capacity & pricing optimization
Predictive maintenance optimization

11. Technologies
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Machine learning
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Machine learning, a branch of artificial intelligence, concerns the
construction and study of systems that can learn from data. For
example, a machine learning system could be trained on email
messages to learn to distinguish between spam and non-spam
messages. After learning, it can then be used to classify new
emailmessages into spam and non-spam folders
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Tom M. Mitchell provided a widely
quoted, more formal definition: "A
computer program is said to learn from
experience E with respect to some
class of tasks T and performance
measure P, if its performance at tasks
in T, as measured by P, improves with
experience E".

12. Technologies
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Machine learning
algorithms can be
organized into a
taxonomy based on the
desired outcome of the
algorithm or the type of
input available during
training the machine

13. Technologies
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Decision tree learning
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A tree showing survival of
passengers on the Titanic ("sibsp"
is the number of spouses or
siblings aboard). The figures under
the leaves show the probability of
survival and the percentage of
observations in the leaf.

14. Technologies
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Natural language processing
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Natural language processing (NLP) is a field of computer science artificial
intelligence and linguistics concerned with the interactions between
computers and human (natural) languages 。

15. Technologies
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terminology

16. Technologies
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Cluster analysis
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The result of a cluster analysis shown as the coloring
of the squares into three clusters.

17. The trends of big data
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Rapid Growth

18. The trends of big data
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Big Data Is The Big Opportunity

19. The trends of big data
Deliver Better Healthcare With Big Data
Quality Of Patient Care
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Legacy System &
Traditional Data
Treatment
Pathways On
Summary Data
International
Results
Treatment
Pathways
On
All The Data
New System &
Big Data
Individual
Patient
History
Social &
Economic
Factors

20. Challenges ahead
Invade User's privacy
 Real time is a real problem
 The Missing Skills triangle
 Easy-to-use big data tools infancy
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21. Challenges ahead
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Invade User's privacy

22. Challenges ahead
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Real time is a real problem

23. Challenges ahead
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The Missing Skills triangle
Computer
Sciences
Business
Data Sciences
Statistics

24. Challenges ahead
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Easy-to-use big data tools infancy

25. Conclusions
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Big data is a Phenomena,is a Methodology.
Big data might be a Challenge,but also is a Chance

26. 
Thanks...