PRODE - Probabilistic declarative process mining

Abstract:

The management of business processes is of extreme importance for supporting efficiency improvements in organisations. Process Mining (PM) deals with the discovery and representation of process models from event data collected from organisations about the executions of their processes. 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.
To accomplish these objectives PRODE builds on the expertise of the research units in the fields of Process Mining, Artificial Intelligence, knowledge discovery, Machine Learning (in particular, Statistical Relational Learning), Logic Programming, Probabilistic Logic Programming, and Answer Set Programming.
Results will be verified through both formal and experimental analysis on a variety of case studies.