A neuro-symbolic approach to computing alignments in Causal Nets

Research theses
Abstract
In the process mining, one of the computationally expensive tasks is computing alignments, i.e., mapping a trace representing the execution of a business process to a model of the business process. This is especially pronounced when using Causal Nets, an expressive model representation requiring making multiple decisions that are not directly reflected in a trace. The goal of the thesis would be to extend an exact approach (e.g., the A* algorithm) with a learned heuristic to efficiently prioritize the algorithm so it performs well in real-world scenarios.
Keywords
process mining
machine learning
neuro-symbolic learning
deep learning
ERC sector(s)
PE Physical Sciences and Engineering
Fields of study
Thesis supervisor
Name supervisor
Jedrzej Potoniec
E-mail
jedrzej.potoniec@put.poznan.pl
Name of Department/Faculty/School
Faculty of Computing and Telecommunications
Name of the host University
Poznan University of Technology (PUT)
EUNICE partner e-mail of destination Research
anna.jaskolska@put.poznan.pl
Country
Poland
Student profile
Thesis level
Master
Minimal language knowledge requisite
English B2
Additional info
Thesis mode
Remote
Start date
Length of the research internship
Other
Financial support available (other than E+)
No mobility is required