Artificial datasets for online Declare discovery

This file contains two datasets. 1. Periodical Sudden Drifts For this case study, we have generated two synthetic logs ( \(\mathcal{L}_1\) and \(\mathcal{L}_2\) ) by modeling two variants of the insurance claim process described in [1] in CPN Tools and by simulating the models. \(\mathcal{L}_1\) contains 14,840 events and \(\mathcal{L}_2\) contains 16,438 events. We merged the logs (eight alternations of \(\mathcal{L}_1\) and \(\mathcal{L}_2\) ) using the Stream Package in ProM (the source code of the package is publicly available at svn.win.tue.nl/repos/prom/Packages/Stream/Trunk).

The same package has been used to transform the resulting log into an event stream. The event stream contains 250,224 events and has several sudden concept drifts (one for every switch from \(\mathcal{L}_1\) to \(\mathcal{L}_2\) ). 2. Gradual Drifts We have considered two variants of the insurance claim process described in [1], \(\mathcal{M}_1'\) (with 21 activities) and \(\mathcal{M}_2'\) (with 19 activities).