FEXUM

Framework for Exploring and Understanding Multivariate Correlations

This repository contains the backend of our big data framework for exploring and understanding:

• Feature selection
• Feature extraction
• Multivariate correlations
• Time series

The frontend and correlation algorithm are located in different repositories.

The basis for this application has been made into a paper, which was submitted to and accepted by ECML PKDD 2017, and published as part of the conference proceedings in the "Lecture Notes in Computer Science" (LNCS) series.

Contributors

• Louis Kirsch
• Niklas Riekenbrauck
• Daniel Thevessen
• Marcus Pappik
• Axel Stebner
• Julius Kunze
• Alexander Meißner

Getting Started

1. Run $ docker-compose build to build all containers (do that when you are changing dependencies)
2. Run $ docker-compose up to start all containers. Put -d for headless mode
3. Run $ docker-compose run web_wsgi python3 manage.py migrate to apply migrations
4. Access the docker ip (mostly localhost) at port 80.

Python Shell

$ docker-compose run web_wsgi python3 manage.py shell

This is useful when you want to test and create database objects, e.g.:

>> from features.tasks import *
>> from features.models import *
>> for feature in Feature.objects.all() 
   ... a_test_name.delay(feature.name)

Task Monitoring

Open the docker host at port 5555

Running with docker in your own network environment

By default the network is configured for the docker containers as followed:

networks:
  fexum:
    ipam:
      driver: default
      config:
        - subnet: 10.151.100.0/24

If this does not work with your network setup, change the subnet.

Development & Contributing

Testing

For testing simply run:

$ docker-compose run web_wsgi py.test

Data Migrations

Create migrations after changing or creating models

$ docker-compose run web_wsgi python3 manage.py makemigrations