Abstract
This article tackles the problem of checking the conformance between a business process model and the data produced by its execution in cases where the data are not given as an event log, but as a set of time series containing the evolution of the variables involved in the process. Tasks in the process model are no longer restricted to the occurrence of a single event, and instead, they can be expressed as a set of temporal conditions about the values of the variables in the log. This causes a paradigm shift in conformance checking (and process mining at a more general level), and because of this, the formalization of both the data and the process model and the algorithms are here redesigned and adapted for this challenging perspective. To illustrate the effectiveness of our approach, an experimental evaluation on a real-world time-series log is carried out, highlighting the benefits of this change of paradigm.
Antonio Gonzalez-Pardo
Associate Professor
Lecturer at the Computer Science Department. Main research interests are related to Computational Intelligence and Metaheuristics applied to Social Networks Analysis, and the optimization of graph-based problems.