CSCE 5300 Introduction to Big data and Data Science ICE-3

CSCE 5300 Introduction to Big data and Data Science

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


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
7. Submission after the deadline is considered as late submission.


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