Resumen

VEGA ROUCO, Leansi; FERNANDEZ RODRIGUEZ, Anel Wilfredo  y  PEREZ ALFONSO, Damián. Algorithm to infer Social Interaction Networks from the execution of a process. Rev cuba cienc informat [online]. 2018, vol.12, n.1, pp.74-89. ISSN 2227-1899.

Process mining is a research discipline that provides techniques for discovering, monitoring and improving business processes in organizations. In process mining, the inference of social interaction networks for a global process is an important element in the behavior analysis of resource interactions, but when such interactions occur in complex processes it is difficult to understand them in their social dimension. This analysis is enriched when performed at the subprocesses. The present work describes an algorithm of social interaction networks inference from events with high number of interactions between resources, which contributes to facilitate the understanding of complex processes in their social dimension. The main contributions of this research are: social interaction networks inference at subprocess level, calculation of factors popularity, efficiency and overload; An add-on for the ProM tool that performs the designed algorithm. The calculation of known metrics to infer social interaction networks and measures of centrality allowed the inference of social interaction networks with the purpose of determining the factors popularity, efficiency and overload of a resource in the network. The correctness of the proposed algorithm was shown by the realization of a study case, obtaining results according to the objective of the research and related to its main contributions.

Palabras clave : Inference metrics; process mining; processes comprehension; social network analysis.

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