Resumen

PEREZ ALFONSO, Damián; YZQUIERDO HERRERA, Raykenler; PUPO HERNANDEZ, Eudel  y  LOPEZ JIMENEZ, Reynaldo. Algorithm for variants process identification. Rev cuba cienc informat [online]. 2015, vol.9, n.4, pp. 199-215. ISSN 2227-1899.

Process mining is a discipline that impulse tools and techniques development for process analysis, starting from event logs. Process mining techniques are used in differents stages of business process management, including diagnosis. Process diagnosis is useful to obtain a general process view, and it’s more significative elements. Event logs characteristics like noise and lack of information affects process mining techniques in process diagnosis stage. On these scenarios identification of control flow patterns become a rough task, so diagnosis objectives can be complicated to achieve. On this work, an algorithm for identification of process models variants is presented. The proposed solution takes into account the noise and lack of information. An experiment was performed with event logs that combine noise and lack of information, using an implementation of the algorithm proposed. Obtained results show that proposed algorithm identifies properly the control flow patterns even on events logs with noise and lack of information.

Palabras clave : algorithm; control flow patterns; process mining; process variants.

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