LERS algorithm in Hadoop MapReduce
Download source code here: http://webpages.uncc.edu/aatzache/ITCS6190/Exercises/HadoopLERS_Example.zip

To run the Code:

1. Create a cluster with Hadoop and Spark in AWS and start the cluster. Once the cluster is running, log-in to the master node using Putty(Windows) or SSH(MAC or Linux). Refer to:
 
GroupActivity_01 Instructions for logging in to the AWS EMR cluster Simple Commands Task2
http://webpages.uncc.edu/aatzache/ITCS6190/Exercises/GroupActivity01_LoggingInto_AWS_Cluster_SimpleCommands_Task2.doc

video: AWS-EMR_Cluster_Setup
https://youtu.be/_A_xEtd2OIM



2. Create a data bucket in AWS S3. Upload data.txt, attributes.txt, parameters.txt from the Data directory and SparkLERS.jar files to S3

3. From the master node download HadoopLERS.jar using the command:
	aws s3 cp s3://BUCKET_NAME/HadoopLERS.jar .

4. Run the .jar file using your terminal or Putty using following command:

	hadoop jar HadoopLERS.jar snippet.Main s3://BUCKET_NAME/attributes.txt s3://BUCKET_NAME/data.txt 
	s3://BUCKET_NAME/parameters.txt s3://BUCKET_NAME/HadoopLERSOutput

5. Download the output folder (HadoopLERSOutput) from S3 to your local machine and upload to Canvas

6. Save all commands in your Terminal Window in a Text File , including your login and username until the last command, with all commands, results and errors if any from the terminal window and upload the   TeminalWindow.txt   to Canvas .

7. Delete/Terminate the AWS cluster and delete all files from S3 when finished, otherwise Amazon will charge your Credit Card