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

20-30 mins setup

2 problems + 1 optional problem

1 extraction - Problem 1 -face and ocr, audio extractor

1 conversion - Problem 3 - collection of old file formats of images,audios

1 combined - Problem 6 - a single script combining together 

1 optional - problem 5

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. 
    •  Rob Kooper will talk to Doug for this if we can spawn 50 VMs on Nebula for the tutorial session. (DONE) We will get  50 VMs on Nebula.
    •  Smruti PadhyOrder 50+ flash drive for back up that will contain the VMs
    •  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
    •  local installation of fence and local authentication.
    •  50 concurrent users to perform conversion/extractions tests
    •  Luigi Marini Max size on File to be uploaded
    •  Not sure of Jetstream yet. 
  • Provide clear instructions as how to access VMs in Nebula with proper credentials.
    •  Clear Instruction of how to access the VMs (e.g., through ssh), from different OSes.
    •  Do we need training accounts on nebula? 
  • (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.
    •  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 or use of IPython notebook
  • 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. 
  •  Create a document for the demo with step-by-step screenshots
  •  Fix the CORS error for file url option (I think it is a known issue)
    
  •  Delay when file is uploaded from local directory
• Create an applications using BD services
  Three applications:
  
  • Problem 1 : Given a collection of images with text embedded in it, try to search images based on its content. (Emphasizes on extraction on unstructured data, indexing and content-based retrieval)
    
    • One can upload images from local directory to obtain images or use external web service.  
      • •  Create an example dataset with images with interesting query
        •  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
      •  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.
      •  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
    • Python
    • Make sure the Elasticsearch is started before the example files are submitted to BD service
      •  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.
      •  Write a python script that will index the technical metadata in ES
    • Search for the image using ES query
      •  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. (Emphasizes on extraction on unstructured data and useful analysis )
    • A collection of text files with reviews
      •  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
      •  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.
      •  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
      •  Provide code for this in stub script
    • Create  separate folders and move the file based on the sentiment value
      •  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.
      
      
  
  
  • Problem 3: Use BD conversion to convert a collection of images/ps/odp files to png/pdf/ppt.  This will demonstrates that if you have a directory with files in old file formats, just use BD to get it all converted. (Emphasies on conversion)
    •  Provide a Python script for this and let Participant use python library to use the  BD service
    
    
  • Problem 4: Given a collection of *.xlsx files, obtain some results based on some columns value. (Emphasizes on extraction and analysis on scientific data)

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• • •    Write a R/Python script to call BD conversion API and get data in *clim format and calculate average air temperature. Also plot a graph of the data. 
      •  Installation of Rstudio server version.
    •  (Optional) to calculate average temperature, call BD extraction service. For this write an extractor that accepts *.clim file and outputs average temperature.
•    Problem 6:  Audio converter , Speech to text extractor

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

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