Abstract
The discovery of predictive models for process performances is an emerging topic, which poses a series of difficulties when considering complex and flexible processes, whose behaviour tend to change over time depending on context factors. We try to face such a situation by proposing a predictive-clustering approach, where different context-related execution scenarios are equipped with separate prediction models. Recent methods for the discovery of both Predictive Clustering Trees and state-aware process performance predictors can be reused in the approach, provided that the input log is preliminary converted into a suitable propositional form, based on the identification of an optimal subset of features for log traces. In order to make the approach more robust and parameter free, we also introduce an ensemble-based clustering method, where multiple PCTs are learnt (using different, randomly selected, subsets of features), and integrated into an overall model. Several tests on real-life logs confirmed the validity of the approach.
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Folino, F., Guarascio, M., Pontieri, L. (2013). Context-Aware Predictions on Business Processes: An Ensemble-Based Solution. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2012. Lecture Notes in Computer Science(), vol 7765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37382-4_15
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DOI: https://doi.org/10.1007/978-3-642-37382-4_15
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