INDIGO

Martin Scheid

Documentation of the Software Campus Project INDIGO

Published at May 11, 2025, 4:32 a.m.

Design Canvas

Justificationary Knowledge

For the design of business processes the semi-formal modeling language "event-driven process chain" (hereinafter: EPK) and "Business Process Model and Notation" (hereinafter: BPMN) have become widely accepted in the business world. There, time-based and factual logical sequences are presented in the form of models. These models are used to document the processes and serve as a basis for further GPM measures. However, there are still a large number of modeling conventions for process design, which are strongly adapted to the needs of individual companies. In workshops for the collection of existing ways, processes are developed with the help of various tools in teams. Examples of utilities used include magnets in appropriate shapes, templates and classic flip charts. Depending on the size of the company, a large number of processes are often changed, which have to be digitized at great expense.
Problem

There are no appropriate possibilities to automatically convert sketched processes on flip charts or whiteboards into machine interpretable formats for further usage in information systems.
Objective

The primary goal is to automate or support the laborious manual and error-prone transfer process.

Idea

A software tool is created which accepts input in different image formats, analyses information about shape, structure and content of the business process and returns it in an adequate format.
Context

A lot of frameworks focus on the analysis of image files. One prominent example is openCV which provides a lot of different algorithms and focuses on image processing. On the other hand the technologies around neural networks picked up speed and a lot of applications in the area of image analysis especially classification came up.
Scope
Design Process

In this project the DSR process according to Kuechler & Vaishnavi will be followed as an example for the design-process-templates which are available in MDP. This Process consists of five steps, starting with the "Awareness of problem"-formulation (I). On this the "Suggestion"-Phase starts (II), followed by the "Development"-phase (III), the "Evaluation"-Phase (IV) and the "Conclusion"-Phase (V). Whenever a phase is skipped or adapted, a statement is inserted why this phase is skipped.
Evaluation

How good are the trained ML-Models to identify a handwritten model? How good is the user experience when using this software?

Method
Artifact

INDIGO software tool for the classification of handwritten process graphs.
Result
Design Knowledge

Instead of the staged algorithms known from state-of-the-art literature convolutional neural networks for picture semantic segmentation are used for the prototype.
  • Iteration 1: Project plan

    Until the submission of the project idea, a project outline must be developed and specified.

  • Suggestion

    Development of a proposal for the first project idea

  • Technical concept

    Creation of the technical concept including the picture to server connection, so the image data can be read and analysed.

  • Scenario enhancement

    To substantiate the benefit of the idea, an application scenario was created. This scenario is also utilized to improve comprehensibility.

  • Consolidation of the overall concept

    Combines the application scenario with the individual aspects of the technical concept

  • Development

    This phase results in an initial proposal for a project outline.

  • Merging the contents

    Merging the contents of the previous phase.

  • Preparation of the work plan
  • Preparation of the exploitation plan
  • Conclusion
  • Iteration 2: Project plan
  • Development

    Revision of the project description

  • Collection of the given feedback

    The feedback of the supervisors is collected and merged so that I simplify the processing.

  • Processing of the given feedback

    Not everything can be included 1:1 in the sketch Some things still have to be adjusted or deleted manually

  • Revision of the project outline

    With the help of the feedback the project outline can now be revised

  • Coordination of the cost plan with the controlling department
  • Conclusion
  • Merge of all contents
  • Finalisation of the project outline
  • Iteration 3: Development of the first prototypes

    Implementation using OpenCV

    test
  • Awareness of Problem
  • Definition of subtasks
  • Literature Review
  • Suggestion
  • Definition of the first prototype based on previous literature reviews
  • Development
  • Development of the first prototype
  • Creation of demo data
  • Evaluation
  • Evaluation using metrics
  • Evaluation through expert interviews
  • Conclusion
  • Summary of the results to date and identification of improvement potentials
  • Iteration 4: Development of the 2nd prototype

    Implementation using Deep Learning techniques

  • Awareness of Problem
  • Literature Review

    The emergence of new technologies, in particular deep learning, should drive the prototype in this direction.

  • Suggestion
  • Conversion of the prototype to Deep Learning

    Based on the good results achieved so far with Deep Learning, the INDIGO prototype is also to be improved through the use of Deep Learning.

  • Implementation of Convolutional Neural Networks
  • Development
  • Implementation of the Deep Learning prototype

    Convolutional Neural Networks are used which are trained with the existing data.

  • Evaluation
  • Evaluation using metrics
  • Evaluation through expert interviews
  • Conclusion
  • Merging the results
  • Iteration 5: Further development of the prototype

    The Deep Learning based prototype has achieved good results, but will need further developed to refine it.

  • Suggestion
  • Proposals for changing the network topology
  • Suggestions for changing the adjustable values
  • Development
  • Adaptation of the network topology
  • Adaptation of variable values
  • Iteration via various settings
  • Evaluation
  • Examination of the metrics of all prototype test runs
  • Evaluation through expert interviews
  • Summary and evaluation of the tests
  • Thesis

    The project idea results from a master thesis where the topic was captured.

  • Feedback from supervisors

    Feedback from the Software AG and the DFKI

  • Application for the Software Campus
  • Submission of the project outline

    Submission to DLR.

  • Final feedback and release of the project outline

    Final feedback and release of the project outline from the Software AG and the DFKI.