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Laboratoire/Entreprise : LAMSADE, Université Paris-Dauphine-PSL
Durée : 3 ans
Contact : daniela.grigori@dauphine.fr
Date limite de publication : 2021-03-30

Contexte :
Process mining is a recent research topic that applies artificial intelligence and data mining techniques to process modelling and analysis [1,2]. The main idea is to extract knowledge from events recorded in an events log in order to discover, monitor and improve processes. Event logs stores activities related to process instances, as well as additional information such as the resources executing the activities, data produced or used, timestamps, or costs.

Process mining approaches allow the discovery of the process model or its variants (a.k.a. discovery), the detection of deviations between the real process and the designed model (a.k.a. conformance checking), and the improvement of the process model based on the observed events (a.k.a. enhancement). Predictive process monitoring is a subfield of process mining that deals with predicting outcome for running instances [3,4].

Most existing process mining and process monitoring approaches consider the process to be in steady state and so do not consider the context in which the process takes place nor the changes that may affect it while being analyzed [5,6]. Information about the context could be derived from the process log (resource occupation rate…) or captured from other sources of information that could enrich the log. Dealing with context information is important to detect and analyze changes [7,8] and is one of the challenges for research described in the Process Mining Manifesto [9].

Sujet :
The aim of this thesis is to consider the context in all the phases of the process improvement life cycle (discovery, conformance checking, enhancement) as well as in process monitoring. Including the context could improve the precision of the discovered process model and of its analysis enabling better recommendation for process improvement and better predictions for process monitoring.

It will also allow to address fairness issues (e.g., not blame an overloaded resource for delays) and conduct causality analysis (e.g., which factor or context variable causes delays).

Towards a context-enhanced analysis of process-centric data, the following objectives should be addressed:
– Propose context-driven process discovery and conformance checking techniques
– Use context attributes to propose meaningful improvements
– Study what context attributes to monitor and how to identify when these attributes change 
– Propose approaches to detect context changes online 
– Propose predictive approaches with online learning to make sure that the process model is to up to date

Profil du candidat :
We seek for excellent and highly motivated student with a background in Computer Science.

Formation et compétences requises :
Master in Computer Science
Required skills : ML and graphs knowledge, programming skills

Adresse d’emploi :
https://www.lamsade.dauphine.fr/fileadmin/mediatheque/lamsade/documents/propositions_theses_2020/grigori.pdf