Assessment of Master Thesis – Academic Consultant
Study programme:Applied Informatics
Field of study:Information Systems Management Academic year:2020/2021
Master Thesis Topic:Visualizing Association Rules Author’s name:Volkan Eymir Akçora
Ac. Consultant’s Name:doc. Ing. Tomáš Kliegr, Ph.D.
Opponent:prof. Ing. Petr Berka, CSc.
Criterion Mark
(1–4)
1. Clarity and comprehensibility of the thesis topic and aims 1
2. The extent and relevance of the description of the current state of knowledge 3
3. The complexity of the thesis topic 1
4. Method adequeteness for solving the given issue, correctness of the choice and use 2
5. The extent, quality and precision of the result description 3
6. Relevance and correctness of the result discussion 3
7. Factual contribution of the thesis result 2
8. Information source relevance and citation correctness 2
9. Logical structure and cohesion among individual parts 3
10. Grammar, linguistic style, terminology and overall arrangement 4
11. Student’s initiative and cooperation with the supervisor 2
12. The use of analytical and data processing methods 2
13. Meeting the principles of ethics and sustainability 1
14. Critical and creative thinking 3
Comments and Questions:
The thesis focuses on the problem of visualization of association rules. It covers the well-studied class of association rules learnt from tabular data and the emerging field of graph-based association rules. The main contribution of the thesis is an implementation of a proof-of-concept script for visualization of graph-based rules and a demonstration of existing tools and techniques for visualization of rules from tabular data. My main concerns relate to the accuracy of the technical descriptions, terminology and the overall linguistic style of parts that show the utility of rule learning for applications in arbitrary information systems. I am also concerned about the generalizability of the code for visualization of the graph-based rules (e.g., ability to visualize longer rules, ability to differentiate between variables and instantiations). In terms of cooperation, I would like to highlight the author’s responsiveness, willingness to iteratively improve the text and code under a timely schedule, although not all feedback was incorporated or not on a completely satisfactory level. Considering that programming and machine learning are not the author’s main field of study, I recommend the thesis for defence.
Conclusion: The Master Thesis is recommended for the defence.
Suggested Grade: 3
Date: 01/06/2021 doc. Ing. Tomáš Kliegr, Ph.D.
Academic Consultant
