Conformance checking using activity and trace embeddings

Conformance checking describes process mining techniques used to compare an event log and a corresponding process model. In this paper, we propose an entirely new approach to conformance checking based on neural network-based embeddings. These embeddings are vector representations of every activity/task present in the model and log, obtained via act2vec, a Word2vec based model. Our novel conformance checking approach applies the Word Mover’s Distance to the activity embeddings of traces in order to measure fitness and precision. In addition, we investigate a more efficiently calculated lower bound of the former metric, i.e. the Iterative Constrained Transfers measure. An alternative method using trace2vec, a Doc2vec based model, to train and compare vector representations of the process instances themselves is also introduced. These methods are tested in different settings and compared to other conformance checking techniques, showing promising results

A Spectrum of Entropy-Based Precision and Recall Measurements Between Partially Matching Designed and Observed Processes

Modern software systems are often built using service-oriented principles. Atomic components, be that web-or micro services, allow constructing flexible and loosely coupled systems. In such systems, services are building blocks orchestrated by business processes the system supports. Due to the complexity and heterogeneity of industrial software systems, implemented processes may deviate from those initially designed. In this paper, we propose a spectrum of conformance measurements. The spectrum results from a generalization of the recently introduced entropy-based approaches for measuring precision and recall between observed process executions and designed process models. The new generalized measures of precision and recall inherit the desired for this class of measures properties and provide analysts with flexible control over the sensitivity for identifying commonalities and discrepancies in the compared processes and performance of the techniques. The reported evaluation based on our implementation of the measures over real-world event logs and automatically discovered models confirms the feasibility of using the approach in industrial settings