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Big data introduction

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Introduction of what bid data is to beginners.

Introduction of what bid data is to beginners.

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  • 1. Faiz ul haque Zeya MS CS University of Tulsa,OK,USA
  • 2. Topics covered           1. Introduction 2.Bigdata: how big it is 3.Bigdata Technology. 4. Few examples of Big Data. 5. Airline reservation system 6. Google Translate. 7.Amazon recommendation. 8. Netflix recommendation. 9. Hadoop, Map reduce. 10. Q&A.
  • 3. Introduction  Large set of data. Site of peta byte, exa byte.  Not stored relational.  Massive scale computational.  NO SQL queries.  New technology like MAP REDUCE,HADOOP.  Reason: Scalability and poor performance on large scale.
  • 4. How large it is  Peta byte 10^15  Zetta byte 10^21 Exabyte 10^ 18  Google processed about 24 petabytes of data per day in 2009.[  Yahoo stores 2 petabytes of data on behavior.  eBay.com uses two data warehouses at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising.
  • 5. BigData Technologies  Relational database,SQL queries cannot handle such amount of data.  Therefore other technologies are requried  MAP REDUCE parallel computation.
  • 6. Few examples of Big Data  Airplane reservation system.  Google Translate.  Netflix Movie recommendation  Amazon Book recommendation
  • 7. Airline reservation system  Oren Etzioni of Washington ‘s venture capital based     startup Farecast. It predicts based on past data whether airline prices will go up or down. Etzioni uses predictive model for that. Microsoft purchase it for 110 M $ Make it part of BING search engine.
  • 8. GOOGLE Translate  Whole internet as training data.Corpus  Google release Trillion word corpus in 2009.  They accept messy data.  Candide uses 3 million translated sentences.  Google uses billions of pages from intenet.
  • 9. Netflix Million $ prize  Netflix announced to award 1M$ prize for the team who improves the recommendation algorithm by 5%.  They are movie recommender.  Most of the sales are due to recommendations from the site.  Reason is that so many shows that the user don’t even know.
  • 10. Amazon’s recommendation  Amazon uses item to item recommendation instead of traditional collaborative recommendation.  Item to item recommendation search for similar items rather than similar users.  This approach is scalable to large data set.
  • 11. Map Reduce
  • 12.  Q&A