[DL] Joint PhD Position at the Fondazione Bruno Kessler and University of Brescia on AI techniques for Process Mining

[Chiara Ghidini]

Position: 1 PhD position
Duration: 3 years
Close Date: 12 September 2014 at 13.00 Italian Time
Web site: http://www.unibs.it/node/9060

One PhD position is open at the Department of Information Engineering, University of Brescia (Italy) on Artificial intelligence and reasoning techniques for process mining in information systems. The research will be carried out jointly between the Department of Information Engineering, University of Brescia and the SHELL team, Fondazione Bruno Kessler (FBK), Trento Italy, where most of the research activities will be conducted. 


About Us

FBK-ICT (www.fbk.eu) conducts research in information technology. Research units and interdisciplinary research projects, such as SHELL (shell.fbk.eu), aim at  addressing important research challenges by exploiting and joining the different scientific competences that are at the base of the internationally well-known scientific excellence of FBK-ICT.

The Department of Information Engineering, University of Brescia conducts research in several areas of information and communication technologies, including computer science and engineering. In this area different groups work on various basic and applied research projects with strong scientific competences especially in the fields of artificial intelligence, information systems, robotics, and human-computer interaction.

For further infos please contact:

Prof. Alfonso Emilio Gerevini, gerevini at ing.unibs.it
Department of Information Engineering
University of Brescia

Dr. Chiara Ghidini, ghidini at fbk.eu
SHELL project
Fondazione Bruno Kessler


Topic Description

TITLE: Artificial intelligence and reasoning techniques for process mining in information systems
REFERENCE PERSONS: Chiara Ghidini, Alfonso Gerevini 
ABSTRACT: Process mining is a recent and rapidly emerging research field with many applications, aiming at discovering, monitoring and improving real processes by extracting knowledge from event logs readily available in today's (information) systems. Despite the several enormous steps carried on in the last years, still a number of open challenges waits to be addressed in this field, as for example, the run-time operational support for processes (i.e., the on-line detection and prediction of problems, and  the run-time provision of recommendations towards their resolution), or the management of complex event logs with different characteristics (e.g., too many, too few or too abstract data).  
The aim of this thesis is investigating how to exploit, adapt and combine techniques and approaches borrowed from different research fields, ranging from logic to artificial intelligence, from model checking to statistics, to advance the existing services for process analysis and process model (re-)design from monitoring data.  To this purpose, several are the challenges to be faced in the work as, for example, (i) the capability to represent and reason about secondary aspects for business processes such as data, time, resources, purpose of data; (ii) the capability to align execution information with models, when they exist, or to mine (logic-based) models from traces, when they do not exist; (iii) the capability to monitor run-time execution and to predict its outcome.  The work will put together theoretical and methodological aspects, including for example the problem conceptualization and representation, as well as implementation and optimization ones, aimed at the development of process analysis services and tools.

SKILLS REQUIRED: good knowledge of artificial intelligence and/or knowledge representation techniques