Metadata-Version: 2.1
Name: prolothar-data-public
Version: 1.11.1
Summary: algorithms for process mining and data mining on event sequences
Home-page: https://anonymous/KI/DataScience/processmining/prolothar-backend
Author: Anonymous
Author-email: anonymous
License: UNKNOWN
Description: # Prolothar
        
        Algorithms for data mining on sequential data like process logs
        
        ## Getting Started
        
        These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
        
        ### Prerequisites
        
        Python 3.7+
        
        ### Installing
        
        ```
        python -m pip install
               -i http://nexus.int.shsservices.de/repository/ki-python-releases/simple
               --trusted-host nexus.int.shsservices.de prolothar-common
        ```
        
        If pip is already configured to use the nexus repository:
        
        ```
        pip install prolothar-common
        ```
        
        ## Running the tests
        
        ```
        coverage run
              --source=prolothar
              -m unittest discover -p prolothar_tests -v
        ```
        
        ## Deployment
        
        When changes are pushed into the master branch, the project is bundled and
        uploaded to our Nexus automatically.
        
        A Docker-Image is also created and pushed into the Gitlab docker registry
        
        ## Versioning
        
        We use [SemVer](http://semver.org/) for versioning.
        
        ## Authors
        
        * **Boris Wiegand** - boris.wiegand@dillinger.biz
        
        See also the list of [contributors](https://gitlab.dillinger.de/KI/DataScience/processmining/prolothar-common/-/graphs/master) who participated in this project.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Process Mining
Description-Content-Type: text/markdown
