Process Discovery using Python (Bachelor’s, SS 2018/2019)

Kontakt

Madhavi Shankar

Name

Madhavi Shankara Narayana

Softwareentwicklerin

Telefon

work
+49 241 80 21949

E-Mail

E-Mail
 

Course Details

Language: The language of the course is English; therefore, all meetings and the written reports will be in English.

Important Dates

Kick-off Meeting

09/04/2019, 14:30 Uhr – 16:00 Uhr

Location: Ahornstrasse 55 52074 Aachen, Main Building (2350), Room: 2010

The attendance to the kick-off meeting is strictly mandatory.

Milestones

The milestones for the project are the following:

  • Milestone 1 - Project Initiation document (deadline Monday 22/04/2019 23:59:59 CET)
  • Milestone 2 - Requirements Specification document (deadline Monday 29/04/2019 23:59:59 CET)
  • Milestone 3 - Design Analysis & dummy P.o.C. (deadline Monday 13/05/2019 23:59:59 CET)
  • Milestone 4 - Sprint 1 code & documentation (from 13/05/2019 to 24/05/2019 (10 days); deadline Friday 24/05/2019 23:59:59 CET)
  • Milestone 5 - Sprint 2 code & documentation (from 24/05/2019 to 07/06/2019 (10 days); deadline Friday 07/06/2019 23:59:59 CET)
  • Milestone 6 - Sprint 3 code & documentation (from 10/06/2019 to 21/06/2019 (10 days); deadline Friday 21/06/2019 23:59:59 CET)
  • Milestone 7 - Testing, assessment and deployment (deadline Monday 01/07/2019 23:59:59 CET)
  • Milestone 8 - Final report on the project (deadline Monday 08/07/2019 23:59:59 CET)

Contact Hours

The proposed contact hours are the following. It is highly advised to ask an appointment to the teaching assistant, preferably in the proposed dates and hours.

Modification to the contact hours will be communicated to students through e-mail and promptly reported on this web-site.

  • Thursday April 11, 14:15-15:45
  • Friday April 12, 14:15-15:45
  • Monday April 15, 14:15-15:45
  • Wednesday April 17, 10:30-12:00
  • Monday April 29, 14:15-15:45
  • Thursday May 2, 14:15-15:45
  • Friday May 3, 10:30-12:00
  • Monday May 6, 14:15-15:45
  • Thursday May 9, 14:15-15:45
  • Friday May 10, 10:30-12:00
  • Monday May 13, 14:15-15:45
  • Thursday May 16, 14:15-15:45
  • Monday May 20, 14:15-15:45
  • Thursday May 23, 14:15-15:45
  • Friday May 24, 10:30-12:00
  • Monday May 27, 14:15-15:45
  • Thursday May 30, 14:15-15:45
  • Friday May 31, 10:30-12:00
  • Monday June 3, 14:15-15:45
  • Thursday June 6, 14:15-15:45
  • Friday June 7, 10:30-12:00
  • Monday June 10, 14:15-15:45
  • Thursday June 13, 14:15-15:45
  • Monday June 17, 14:15-15:45
  • Thursday June 20, 14:15-15:45
  • Friday June 28, 10:30-12:00

Examination dates

The final oral examination will be set as a personal appointment between Monday 08/07/2019 and Wednesday 17/07/2019.

Introduction

Process Mining is a growing branch of Data Science that focuses on analyzing event data recorded in Information Systems, focusing on the process perspective.

Investments in Process Mining from public and private companies are steadily increasing, and are expected to more than double in the next five years.

Hence a good knowledge of Process Mining is an important skill for Data Scientists.

Process discovery is the initial and one of the most challenging process mining tasks. Based on an event log, a process model is constructed thus capturing the behaviour seen in the log.

This Software lab course is designed to enable students to get their hands on the discovery process. This course includes implementation of the algorithms either to discover or enable in discovering the process. Process Discovery involves the core discovery algorithms and their visualizations.

This Software Lab course includes tutorials describing the existing ProM Framework and Process discovery techniques/visualizations.

The course will use Python as the core language for implementation. It is expected that the students will follow the Software engineering principles during the course term.

Introductory Sessions

All the above topics will be introduced in brief. Participation is mandatory throughout the course. In the introductory sessions, topics will be assigned to the students and deadline for submitting the report and implementation will be discussed.

Groups will be formed to work on the assignments

Student work structure

Students will be required to understand and implement the assignment requirements in Python and provide proper visualizations. A proper SDLC lifecycle will be followed during this phase to track the development. The details of the methodology will be communicated in the introductory session.

A written report on the implementation, its advantages and issues should be produced individually by the students.

Grading

The grading will take into account the written report and the Python model implemented.

Prerequisites

  • Software Engineering knowledge(Design, development and testing)
  • Prior programming experience. Not necessarily Java or Python.
  • Interest to learn and code in Python

Optionals

• Coursera "Process Mining: Data Science in Action" course

• Business Process Intelligence (BPI) Course

• Introduction to Data Science (IDS) Course

Resources

Registration

The registration is carried out by the central registration process in January 2019

In order to increase your chance for being elected for this lab, please state your qualifications, experiences, and overall grades in your enrolled study as detailed as possible. Please give clearly why you are definitely a suitable candidate for this lab.

You will be informed about the first meeting in the weeks after the registration closes.