Generative Data Intelligence

(Submitted on 26 Mar 2020)

Abstract: In process mining, process models are extracted from event logs using process
discovery algorithms and are commonly assessed using multiple quality
dimensions. While the metrics that measure the relationship of an extracted
process model to its event log are well-studied, quantifying the level by which
a process model can describe the unobserved behavior of its underlying system
falls short in the literature. In this paper, a novel deep learning-based
methodology called Adversarial System Variant Approximation (AVATAR) is
proposed to overcome this issue. Sequence Generative Adversarial Networks are
trained on the variants contained in an event log with the intention to
approximate the underlying variant distribution of the system behavior.
Unobserved realistic variants are sampled either directly from the Sequence
Generative Adversarial Network or by leveraging the Metropolis-Hastings
algorithm. The degree by which a process model relates to its underlying
unknown system behavior is then quantified based on the realistic observed and
estimated unobserved variants using established process model quality metrics.
Significant performance improvements in revealing realistic unobserved variants
are demonstrated in a controlled experiment on 15 ground truth systems.
Additionally, the proposed methodology is experimentally tested and evaluated
to quantify the generalization of 60 discovered process models with respect to
their systems.