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Applying Clustering in Process Mining to Find Different Versions of a Business Process That Changes over Time
(Springer, 2012) Luengo Mundaca, Daniela Lorena; Sepúlveda Fernández, Marcos Ernesto
Most Process Mining techniques assume business processes remain steady through time, when in fact their underlying design could evolve over time. Discovery algorithms should be able to automatically find the different versions of a process, providing independent models to describe each of them. In this article, we present an approach that uses the starting time of each process instance as an additional feature to those considered in traditional clustering approaches. By combining control-flow and time features, the clusters formed share both a structural similarity and a temporal proximity. Hence, the process model generated for each cluster should represent a different version of the analyzed business process. A synthetic example set was used for testing, showing the new approach outperforms the basic approach. Although further testing with real data is required, these results motivate us to deepen on this research line.
Experimenting with an OLAP Approach for Interactive Discovery in Process Mining
(2015) Pizarro, Gustavo; Sepúlveda Fernández, Marcos Ernesto