  Existing well investigated Predictive Process Monitoring techniques typically
construct a predictive model based on past process executions, and then use it
to predict the future of new ongoing cases, without the possibility of updating
it with new cases when they complete their execution. This can make Predictive
Process Monitoring too rigid to deal with the variability of processes working
in real environments that continuously evolve and/or exhibit new variant
behaviours over time. As a solution to this problem, we evaluate the use of
three different strategies that allow the periodic rediscovery or incremental
construction of the predictive model so as to exploit new available data. The
evaluation focuses on the performance of the new learned predictive models, in
terms of accuracy and time, against the original one, and uses a number of real
and synthetic datasets with and without explicit Concept Drift. The results
provide an evidence of the potential of incremental learning algorithms for
predicting process monitoring in real environments.
