FromMLWFConfigurationToBPMN

Overview

The ml2wf project aims at helping Data Scientists to create their workflows in the BPMN standard from a FeatureModel and to capitalize on their knowledge from new BPMN workflows.

You can define your own constraints easily by commenting your workflow using a predefined and customizable syntax.

Once created and modified, the data scientist can merge the workflow into the FeatureModel and reuse it later.

Furthermore, it will allow data scientists to reduce meta-learning space and, over time, automate the creation of workflows tailored to a given time series anomaly detection problem.

Table of contents

• Overview
• Usage
  • Command line
  • Configuration
• Installation
  • Prerequisites
  • Build
  • Run
• Example

Usage

Command line

NAME

ml2wf  Machine Learning problem to Workflow

SYNOPSIS

ml2wf generate -i file -o directory [-v level]
ml2wf build -f FeatureModel -m metaDirectory -i instanceDirectory [-b] [-v level]
ml2wf merge [--meta|--instance] -i file -o FeatureModel [-fb] [-v level]
ml2wf save -i meta instance -o FeatureModel [-b] [-v level]

DESCRIPTION

Commands

generate  generate a workflow
build     build a FeatureModel from a set of workflows
merge     import a worklow in a FeatureModel
save      save a meta-workflow and its instance in a FeatureModel

Arguments

-i, --input       input path
-o, --output      output path
-b, --backup      backup the original FeatureModel file before any modification
-f, --full        process a full merge (including meta/instance relationship)
-v, --verbose     verbose mode (0=OFF,1=FATAL,2=ERROR,3=WARN,4=INFO,5=DEBUG,6=TRACE,7=ALL)

Configuration

The default configuration is the following :

# constraints syntax (name : arity : symbol)

before : 2 : >>
after : 2 : <<

imp : 2 : =>
equ : 2 : <=>

conj : 2 : &
disj : 2 : |

not : 1 : !

Note that you can change this configuration modifying the configuration.cfg under the ml2wf/config directory

Installation

Prerequisites

JDK 11

Maven

Build

cd ml2wf
mvn clean package

The generated .jar is located in the target directory.

Run

cd target
java -jar ml2wf.jar [command] [arguments]

Example

Lets consider this generic workflow : my_wf.bpmn2 :

and this initial feature model : featureModel.xml :

Step 1 : Instantiation & Modification

We instantiate our generic workflow using the generate command :

java -jar ml2wf.jar generate -i my_wf.bpmn2 -o result/

Note that the resulting instance will be located in the result directory with the name my_wf_instance.bpmn2.

We change the tasks names which give us :

Note that we put some constraints on our tasks (in comments).

=> We now have 3 possibilities to create our knowledge database (FeatureModel).

Step 2-A : Merging

We can merge our workflows in the FeatureModel using the merge command :

java -jar ml2wf.jar merge --meta -f -i my_wf.bpmn2 -o featureModel.xml
java -jar ml2wf.jar merge --instance -f -i result/my_wf_instance.bpmn2 -o featureModel.xml

Note that the -f argument will merge the meta/instance relationship

Beware a merge-instance only works properly if you use a generated workflow. Indeed in the XML (and not directly visible) _</bpmn2:task>_ gives information about the fact that the task must be considered as an instance, which will transform it into a concrete Feature. You can see the properties of a task by double-clicking on it.

Step 2-B : Saving

We can save the meta and instance relationship in the FeatureModel using the save command :

java -jar ml2wf.jar save -i my_wf.bpmn2 result/my_wf_instance -o featureModel.xml

Note that like any other command, unless the --backup (-b) argument is specified, the original FeatureModel will be modified without backing it up.

Step 2-C : Building

We can build the FeatureModel using our existing workflows and the build command :

java -jar ml2wf.jar build -f featureModel.xml -m my_wf.bpmn2 -i result/

Note that the -m/-i argument can be a directory. In that case, all workflows in the given directory will be merged in the FeatureModel.

=> Here is the result :

Note that the workflows' constraints are translated into FeatureModel constraints in order to keep our knowledge database consistency.

Step 3 : Reusing your generated tasks for other workflows

Using the FeatureIDE, you now can select the wished tasks and it will automatically select the needed ones.