Artem Polyvyanyy

TL;DR: This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory that allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs.

TL;DR: This paper presents a conceptual framework that can be instantiated into a concrete approach for estimating the simplicity of a model, considering the behavior the model describes, thus allowing a more holistic analysis.

TL;DR: In this article, the authors use the connectivity property of graphs to achieve an efficient and extensive discovery of the compositional structure of behavioral models; behavioral models get stepwise decomposed into components with clear structural characteristics and inter-component relations.

TL;DR: In this article, the authors propose a spectrum of conformance measurements for measuring precision and recall between observed process executions and designed process models, which inherit the desired for this class of measures properties and provide analysts with flexible control over the sensitivity for identifying commonalities and discrepancies in compared processes and performance of the techniques.

TL;DR: In this article, the authors address the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another.