Process Conformance is a crucial step in the area of Process Mining:
the adequacy of a model derived from applying a discovery algorithm
to a log must be certified before making further decisions that affect
the system under consideration.
In the first part of this thesis, among the different conformance
dimensions, we propose a novel measure for precision, based on the
simple idea of counting these situations were the model deviates from
the log. Moreover, a log-based traversal of the model that avoids
inspecting its whole behavior is presented. Experimental results show
a significant improvement when compared to current approaches for
the same task. Finally, the detection of the shortest traces in the
model that lead to discrepancies is presented.
In the second part of the thesis, two different approaches are proposed
in order to use the precision analysis information for refining
the model, improving its accuracy. The first one is based on the idea
of break concurrencies reflected in the model but not in the log. The
second one presents the Supervisory Control Theory as the mechanism
to improve the accuracy of the models building supervisors for
controlling the precision issues.
