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- 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
- 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
- We will provide a VM with everything pre-installed in it through Nebula.
- 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)
- 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
- 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
- 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.
- Provide Instructions to start Elasticsearch and start a webclient to it for visualization.
- 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
- One can upload images from local directory to obtain images or use external web service.
- 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.
- A collection of text files with reviews
- 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)
- 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)
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