The modelling of business processes is pivotal to support decision-making in complex industrial and corporate domains.

Recent years have seen the birth of the Business Process Management research area, focused on the analysis and control of process execution quality, and in particular, the rise in popularity of Process Mining, which encompasses a set of techniques to extract valuable information from event logs. 

The rise in popularity is testified by a significant growth in research conducted on the development and improvement of algorithms in the last 25 years, by the birth of dedicated Conference Series, by important academic and industrial frameworks for supporting a wide variety of Process Mining techniques and types of models, and by recent studies on  evidence on the application of Process Mining in organisations.

The PRODE project stands at the forefront of enhancing business process management through the innovative use of Process Mining techniques. Central to its mission is the exploration and implementation of declarative process models derived from event data, emphasizing binary supervised learning within the context of uncertainty. 

A key innovation of PRODE lies in its operational framework, which is grounded in computational logic, specifically combining the strengths of Probabilistic Logic Programming and Answer Set Programming. This fusion is designed to significantly enhance the expressive capabilities of DECLARE frameworks, thereby improving the reasoning and learning tasks associated with process modeling. Through this approach, PRODE intends to tackle real-process issues marking a significant leap towards more effective and comprehensive process management solutions.

PRODE aims to address critical challenges in the business process management field,  balancing the precision and understandability of models, managing inherent log uncertainties, navigating compliance issues through probabilistic assessments, and optimizing model selection to enhance workflow management. 

Drawing on extensive expertise in Process Mining, Artificial Intelligence, Machine Learning, and Logic Programming, PRODE is committed to validating its findings through rigorous formal and empirical analyses across diverse case studies. This initiative not only aims to refine process management practices but also to contribute significantly to the fields of knowledge discovery and organizational efficiency.

The project addresses the task of learning and reasoning upon declarative process models, within the setting of binary supervised learning, taking into account also uncertainty. With the aim of providing viable solutions, the PRODE project will focus on the following issues in particular:

1) exploit the availability of positive and negative examples: in many cases, user experts provide examples with desired and undesired behaviour (hence the labels “positive” and “negative”), but the majority of the discovery approaches exploits only the positive set;

2) precision and understandability of discovered models: precise models could perfectly discriminate between positive and negative examples, but might turn out to be too complex for the final user. We might want to learn models which do not perfectly discriminate between positive and negative examples, but which are simpler and understandable for the final user: probabilistic approaches might help to simplify the models, yet providing a clear and formal semantics;

3) deal with the uncertainty that real logs usually bear: on one side, logs are just a partial incomplete view of the reality; on the other side, the information in the log might be incomplete, partially specified, and even non trustable;

4) compliance issues: while many approaches provide a crisp yes/no answer to the question if a trace “is conformant” with a model, we will explore the possibility of returning a score representing the probability/degree of a trace to be compliant to the model;

5) model selection issues: as there can be multiple output models from the process discovery task, with an associated uncertainty as a result of point 3), we might want to identify the preferable models, in order to improve the workflow management.

The PRODE project will take advantage of the development of works in the fields of Probabilistic Logic Programming (PLP) and Answer Set Programming (ASP), and will build a set of techniques that target the issues above by means of new combinations of declarative Process Mining with probabilistic and combinatorial approaches. The final aim is to produce more verifiable and understandable explanations of its processes to an organisation.

Results will be verified through both formal and experimental analysis on a variety of case studies.