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A web platform that helps a team of researchers to collect label data for video files.

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Video Knowledge Collector

This is a Rails 5 based standalone web application that helps a team of researchers to collect label data for video files and build a knowledge base graph that contains all collected data.

Set up

Installing Ruby

Make sure to install Ruby 2.5 to use this Rails application. We recommend you to install Ruby on your environment using RVM. Instructions can be found in RVM homepage.

You can check if you have the right version of Ruby by:

$ ruby -v
ruby 2.5.0

Installing SQLite3

To run this application in a development mode, you need to have SQLite3 installed on your local environment.

Check out if you have SQLite3 installed.

$ sqlite3 --version

Installing Rails

To install rails, use the gem install command provided by RubyGems:

$ gem install rails

After installing the Rails, verify that you have installed Rails correctly.

$ rails --version

This should show something like "Rails 5.1.1".

Installing the dependencies

After installing Ruby and Rails, please install all internal dependencies for this Ruby project.

$ bundle install

Initializing the database

Before starting up this application in development mode, you also need to initialize the database to use in the development mode.

Simply run db:migrate script to initialize the database.

$ bin/rails db:migrate

Development

Start development server

This application can be started in development mode with the following command.

$ bin/rails server

Find more information on this command on Ruby on Rails Guides.

Start development console

This development console for this application can be started with the following command.

$ bin/rails console

Find more information on this command on Ruby on Rails Guides.

Deployment

There are many options for deploying the Rails 5 app on the server.

Our recommended option is to use Puma and Nginx. Refer to this instruction by Digital Ocean for further information: How To Deploy a Rails App with Puma and Nginx on Ubuntu 14.04.

Acknowledgements

This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (2017-0-01780, The technology development for event recognition/relational reasoning and learning knowledge based system for video understanding)

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