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.
- Louis Kirsch
- Niklas Riekenbrauck
- Daniel Thevessen
- Marcus Pappik
- Axel Stebner
- Julius Kunze
- Alexander Meißner
- Run
$ docker-compose buildto build all containers (do that when you are changing dependencies) - Run
$ docker-compose upto start all containers. Put-dfor headless mode - Run
$ docker-compose run web_wsgi python3 manage.py migrateto apply migrations - Access the docker ip (mostly localhost) at port 80.
$ docker-compose run web_wsgi python3 manage.py shellThis 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)Open the docker host at port 5555
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.
For testing simply run:
$ docker-compose run web_wsgi py.testCreate migrations after changing or creating models
$ docker-compose run web_wsgi python3 manage.py makemigrations
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