Implementation of a Scalable Interactive Event Data Visualization in Python
BSc Thesis Project
Title: Implementation of a Scalable Interactive Event Data Visualization in Python
Author: Humam Kourani
1st Examiner: Prof.dr.ir. Wil M.P. van der Aalst
2nd Examiner: Prof. Dr. rer. nat. Martin Grohe
Daily Supervisor: Dr.ir. Sebastiaan J. van Zelst
This thesis concerns the implementation of a scalable interactive event data visualization in the programming language Python, as part of the PM4Py (Process Mining for Python) project. The visualization program is able to handle (huge) real data sets, providing valuable insights inside them. Such insights allow process owners to get advanced information on the execution of their processes. This document starts with explaining the importance of event data visualization techniques. Then, some related work on other already implemented visualization tools is discussed. After that, the architecture of our program is introduced and three challenges that have emerged during the implementation of the program are addressed. Moreover, an overview of how the program can be called and how it works is given. Our program consists of five Python files. The implementation of all functions of these files is explained briefly in the document as well. Finally, some evaluation results are addressed: a case study on the performance of the program and an experiment on the effect of memory limitation on the behavior of the program. This thesis may be of interest to data analysts in particular, but to data scientists and computer scientists as well.