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An Insight into Map Reduce and related technology Renjith Peediackal 09BM8040 Project Guide: Prof. Prithwis Mukherjee
[object Object],[object Object],[object Object],Goals
The case for Map Reduce
Recommendation System: Older questions ,[object Object],[object Object],[object Object],[object Object]
A lot more questions ,[object Object],What pages are my customer’s reading
A lot more questions contd.. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
And the typical problems with recommendation systems
Problems with popularity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mixing expert opinion ,[object Object],[object Object],[object Object]
Pearls of wisdom in the net
But internet data is unfriendly ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The case for a new technique ,[object Object],[object Object]
What is Data in flight? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Map and reduce ,[object Object],[object Object]
Terminology explained.. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Word count and twitter ,[object Object],[object Object],[object Object],[object Object]
How does a map reduce programme work Programmer has to specify two methods: Map and Reduce
map (k, v) -> <k', v'>* ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Shuffle or sort ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
– reduce (k', <v'>*) -> <k', v'>* ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How this new way helpful for our recommendation system? ,[object Object],[object Object],[object Object],[object Object]
Maps into a denser smaller table
Fault tolerance two different types- Database school of thought
Fault tolerance two different types- MR school of thought
Hierarchy of Parallelism: Cycle of brute force fault tolerance
Criticisms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why it is valuable? ,[object Object],[object Object],[object Object],[object Object]
Why can’t parallel DB deliver the same? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
An example: Invite you to the complexity-sequential web access-based recommendation system
sequential web access-based recommendation system ,[object Object]
Recommendation ,[object Object],[object Object],[object Object],[object Object]
Some details ,[object Object],[object Object],[object Object],Session ID Web access sequence 1 abdac 2 eaebcac 3 babfae 4 abbacfc
Details Access events can be classified into frequent and infrequent based on frequency crossing a threshold level And a tree consisting of frequent access events can be created. Length of sequence Sequential web access pattern with support 1 a:4. b:4, c:3 2 aa:4. ab:4. oc3. ba:4. bc:3 3 aac:3, aba;4, obc:3, bac:3 4 Abac:3
 
The Map and reduce ,[object Object],[object Object],[object Object],[object Object],[object Object]
DBMS for the same case? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Parallel DB vs MapReduce ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary of advantages of MR ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Is mapReduce the final word?
What is hadoop ,[object Object],[object Object]
Pig ,[object Object],[object Object],[object Object],[object Object]
What does hive do? ,[object Object],[object Object],[object Object],[object Object]
Hive architecture
New models : cloud Map reduce for dummies! ,[object Object],[object Object],[object Object],[object Object]
Concerns ,[object Object],[object Object],[object Object],[object Object]
Research Paper 1 MapReduce and Parallel DBMSs:Friends or Foes? Michael St onebraker, Daniel Abad i, Dav id J. eWitt, Sam Maden, Erik Paulson,Andrew Pav lo, and Alexander Rasin ,[object Object],[object Object],[object Object],[object Object],[object Object]
Research Paper 2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Research Paper 3 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Research Paper 4 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Research Paper 5 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Research Paper 6 Hive – A Petabyte Scale Data Warehouse Using Hadoop Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu and Raghotham Murthy ,[object Object],[object Object],[object Object]
Research Paper 7 Massive Structured Data Management Solution Ullas Nambiar, Rajeev Gupta, Himanshu Gupta and Mukesh Mohania IBM Research - India ,[object Object],[object Object],[object Object]
Research Paper 8 Situational Business Intelligence Alexander Löser, Fabian Hueske, and Volker Markl TU Berlin Database System and Information Management Group ,[object Object],[object Object],[object Object],[object Object]
Research Paper 9 Beyond Search - Web Scale Business Analytics Alexander Löser http://user.cs.tu-berlin.de/~aloeser ,[object Object],[object Object],[object Object],[object Object]
Research Paper 9 An Intelligent Recommender System using Sequential Web Access Patterns Alexander Löser http://user.cs.tu-berlin.de/~aloeser ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Web sites references
 

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Map Reduce amrp presentation

  • 1. An Insight into Map Reduce and related technology Renjith Peediackal 09BM8040 Project Guide: Prof. Prithwis Mukherjee
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  • 3. The case for Map Reduce
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  • 7. And the typical problems with recommendation systems
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  • 10. Pearls of wisdom in the net
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  • 17. How does a map reduce programme work Programmer has to specify two methods: Map and Reduce
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  • 22. Maps into a denser smaller table
  • 23. Fault tolerance two different types- Database school of thought
  • 24. Fault tolerance two different types- MR school of thought
  • 25. Hierarchy of Parallelism: Cycle of brute force fault tolerance
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  • 29. An example: Invite you to the complexity-sequential web access-based recommendation system
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  • 33. Details Access events can be classified into frequent and infrequent based on frequency crossing a threshold level And a tree consisting of frequent access events can be created. Length of sequence Sequential web access pattern with support 1 a:4. b:4, c:3 2 aa:4. ab:4. oc3. ba:4. bc:3 3 aac:3, aba;4, obc:3, bac:3 4 Abac:3
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  • 39. Is mapReduce the final word?
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