04 Feb CSCE 5300 Introduction to Big data and Data Science ICE-3 ? Lesson Title: Hadoop MapReduce and Hadoop Distributed File Sys
CSCE 5300 Introduction to Big data and Data Science
ICE-3
Lesson Title: Hadoop MapReduce and Hadoop Distributed File System (HDFS)
Lesson Description: Overview of Hadoop and Map Reduce Paradigm. The Lesson focuses on
map reduce applications with coding exercises by actual implementation
In class exercise
1. Matrix Multiplication in Map Reduce
Suppose we have a i x j matrix M, whose element in row i and column j will be denoted and
a j x k matrix N whose element in row j and column k is donated by then the product P = MN
will be i x k matrix P whose element in row i and column k will be donated by ,
where = .
1. Create a Map-Reduce Program to perform the task of matrix multiplication
Reference:
https://lendap.wordpress.com/2015/02/16/matrix-multiplication-with-mapreduce/
2. Breadth First Search using Map Reduce
3. Depth First Search using Map Reduce
4. Apply Map reduce problem using K-Means Clustering Technique. A view
point of the such algorithms are presented in the screenshot.
Convert this into code and use right dataset to implement this scenario.
Marks will be distributed between logic, implementation and UI
Programming elements:
Hadoop MapReduce and HDFS
Source Code:
Given in canvas.
ICE Submission Guidelines
1. ICE Submission is individual.
2. ICE code has to be properly commented.
3. The documentation should include the screenshots of your code/results with explanation.
4. Provide the explanation of the dataset/exercise as per your understanding.
5. The similarity score for your document should be less than 15%.
6. All you need to do is submit the source code (properly commented) and documentation
(.pdf/.doc) with explanation and screenshot of source code having input logic and output
results.
7. Submission after the deadline is considered as late submission.
CSCE 5300 Introduction to Big data and Data Science ICE-3
Lesson Title: Hadoop MapReduce and Hadoop Distributed File System (HDFS) Lesson Description: Overview of Hadoop and Map Reduce Paradigm. The Lesson focuses on map reduce applications with coding exercises by actual implementation In class exercise
1. Matrix Multiplication in Map Reduce
Suppose we have a i x j matrix M, whose element in row i and column j will be denoted and a j x k matrix N whose element in row j and column k is donated by then the product P = MN will be i x k matrix P whose element in row i and column k will be donated by , where = .
1. Create a Map-Reduce Program to perform the task of matrix multiplication Reference: https://lendap.wordpress.com/2015/02/16/matrix-multiplication-with-mapreduce/
2. Breadth First Search using Map Reduce 3. Depth First Search using Map Reduce
4. Apply Map reduce problem using K-Means Clustering Technique. A view point of the such algorithms are presented in the screenshot. Convert this into code and use right dataset to implement this scenario.
Marks will be distributed between logic, implementation and UI Programming elements: Hadoop MapReduce and HDFS Source Code: Given in canvas.
ICE Submission Guidelines
1. ICE Submission is individual. 2. ICE code has to be properly commented. 3. The documentation should include the screenshots of your code/results with explanation. 4. Provide the explanation of the dataset/exercise as per your understanding. 5. The similarity score for your document should be less than 15%. 6. All you need to do is submit the source code (properly commented) and documentation
(.pdf/.doc) with explanation and screenshot of source code having input logic and output results.
7. Submission after the deadline is considered as late submission.
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