Big Data

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

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Big Data

  1. 1. Big Data Group C: Wei Luo,JunHao Min,ZhiXiang Guo JiaQiang Dong,Hui Huang WHUT
  2. 2. What is Big Data  we are a part of it every day
  3. 3. What Does Big Data Look Like
  4. 4. In Practice     Cloud or in-house? Big data is big Big data is messy Culture
  5. 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 
  6. 6. Automotive      Data warehouse optimization Predictive asset optimization Connected vehicle Actionable customer insight Telecommunications      active call center Smarter campaigns Network analytics Location-based services
  7. 7.  Banking      Optimize offers and cross sell Contact center efficiency and problem resolution Payment fraud detection and investigation Counterparty credit risk management Insurance     Create a customer-focused enterprise Optimize enterprise risk management Optimize multi-channel interaction Increase flexibility and streamline operations
  8. 8.  Consumer Products   Micro-market campaign management   Optimized promotions effectiveness Real-time demand forecast Oil & Gas  Advanced condition monitoring  Drilling surveillance & optimization  Production surveillance & optimization
  9. 9.  Energy and Utilities       Distribution load forecasting and scheduling Create targeted customer offerings Condition-based maintenance Enable customer energy management Smart meter analytics Government     Threat prediction and prevention Social program fraud, waste and errors Tax compliance - fraud and abuse Crime prediction and prevention
  10. 10.  Healthcare     Measure and act on population health Engage consumers in their healthcare Health monitoring and intervention Travel & Transportation    Customer analytics and loyalty marketing Capacity & pricing optimization Predictive maintenance optimization
  11. 11. Technologies  Machine learning  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  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. 12. Technologies  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. 13. Technologies  Decision tree learning  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. 14. Technologies  Natural language processing  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. 15. Technologies  terminology
  16. 16. Technologies  Cluster analysis  The result of a cluster analysis shown as the coloring of the squares into three clusters.
  17. 17. The trends of big data  Rapid Growth
  18. 18. The trends of big data  Big Data Is The Big Opportunity
  19. 19. The trends of big data Deliver Better Healthcare With Big Data Quality Of Patient Care  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. 20. Challenges ahead Invade User's privacy  Real time is a real problem  The Missing Skills triangle  Easy-to-use big data tools infancy 
  21. 21. Challenges ahead  Invade User's privacy
  22. 22. Challenges ahead  Real time is a real problem
  23. 23. Challenges ahead  The Missing Skills triangle Computer Sciences Business Data Sciences Statistics
  24. 24. Challenges ahead  Easy-to-use big data tools infancy
  25. 25. Conclusions   Big data is a Phenomena,is a Methodology. Big data might be a Challenge,but also is a Chance
  26. 26.  Thanks...

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