Proposal submitted: XSEDE16TutorialProposal_v1.docx

Abstract

When: July 18th

Where

Tutorial Session Design

Introduction to Brown Dog (30m)

This is a presentation and demo of Brown Dog project and service

How to use Brown Dog Services (1h 30m)

This is a session to teach how to use Brown Dog Services

• Participant will use his/her own laptop for this part
  • We will provide a VM with everything pre-installed in it through Nebula. 
    • TODO: Rob will talk to Doug for this if we can spawn 50 VMs on Nebula for the tutorial session
    • TODO: Order 50+ flash drive for back up that will contain the VMs
    • TODO: Create a VM with everything installed in it and take a snapshot which will then be deployed within Nebula. Approx. time required - 2 days
      • Make a list of all softwares required and the directory structure for the tutorial
    • TODO: Not sure of Jetstream yet. 
  • Provide clear instructions as how to access VMs in Nebula with proper credentials.
    • TODO: Clear Instruction of how to access the VMs (e.g., through ssh), for different OSes.
  • (Before tutorial - wiki pages with clear instructions) Installs Python/R/MATLAB/cURL to use BD Service along with the library required in case any one interested in using the BD services in future.
    • TODO: Create wiki pages with clear instructions
• Demonstration of use of BD Fiddle 
  • Sign up for Brown Dog Service
  • Obtain a key/token using curl or Postman
  • Use token and bd fiddle interface to obtain to see BD in action. 
  • Copy paste the python code snippet and use it the application to be explained next. 
  • TODO: Create a document for the demo with step-by-step screenshots
    
    
• Create an applications using BD services
  Three applications:
  
  • Problem 1 : Given a collection of images with text embedded in it (or scanned handwritten documents image), try to search images based on its content.
    
    • One can upload images from local directory to obtain images or use external web service.  
      • • TODO: Create an example dataset with images
        • TODO: Provide a code snippet of using externel service to obtain images. e.g. Flicker API.
          •  This will only be provided as an example and will not be used for the rest of the code.
          
          
    • Let the participant use the python library of BD to obtain key/token and submit request to BD-API gateway
      • TODO: Provide the link to the current BD REST API and create a document/wiki page showing step-by-step screenshots of obtaining a key/token using python library.
      • TODO: Write a Python script that will serve as a stub for the BD client
        • • The participant will fill in the code to BD REST API call to submit their requests.
    • Make sure OCR and face extractor are running before starting the demo
    • Make sure the Elasticsearch is started before the example files are submitted to BD service
      • TODO: Provide Instructions to start Elasticsearch and start a webclient to it for visualization.
        • Make sure the cluster name in the config.yml differs for each participant.
    • Once technical metadata is obtained from BD, index it tags and technical metadata in an locally running Elasticsearch.
      • TODO: Write a python script that will index the technical metadata in ES
    • Search for the image using ES query
      • TODO: Provide ES query for search
  • Problem 2 : Given a collection of text files from a survey or reviews for a book/movie, use sentiment analysis extractor to calculate the sentiment value for each file and group similar values together.
    • A collection of text files with reviews
      • TODO: Obtain an examples dataset from the web.
    • Let the participant use the python library of BD to obtain key/token and submit request to BD-API gateway
      • TODO: Provide the link to the current BD REST API and create a document/wiki page showing step-by-step screenshots of obtaining a key/token using python library.
      • TODO: Write a Python script that will serve as a stub for the BD client
        • • The participant will fill in the code to BD REST API call to submit their requests.
    • Make sure the Sentiment Analysis extractor is running
    • Saves the results for each text file in a single file with corresponding values
      • TODO: Provide code for this in stub script
    • Create  separate folders and move the file based on the sentiment value
      • TODO: Provide a code that will do the above action in the stub
    • (Optional) Index text files along with the sentiment values and use ES visualization tool to search for documents with sentiment value less than some number.
    • Tried to see Yelp API, IMDB API to obtain reviews (???)  or use Twitter API (??) to pull some reviews
  • Problem 3: Use BD convert to convert a collection of images with old format to png or pdf. convert odp/odt to ppt/doc
                 
  • CSV files (Ameriflux), use BD for some gap-filing on the files and return result.
    
    
  • Think of a R client for BD. (PeCAn??)
    
    
  • Think of a MATLAB client 
    
    
  • Want to build a Web application on top of BD, (Similar to what Marcus build??)
    
    
  • Use BD convert to convert a collection of images with old format to png or pdf. convert odp/odt to ppt/doc

How to add Your Tool to Brown Dog Services (1h 30m)

This is a session to teach how to add user's tool to Brown Dog Services

Use Contributors Landing Page for this part of the session.

Participant will be provided with a VM with all required setup so that they can create their own tool. They will require to install VirtualBox to use the VM.

Participants will be uploading their Tools  to the Tools Catalog. 

As an example of this part, try to think of an example for creating a BD tool.