This extended abstract introduces the Xcavate ProM plug-in that is built on top of the DiSCover plug-in and the Log Skeleton filter plug-in. The Xcavate plug-in allows the user to specify two ranges of noise levels, to discover a Petri net for every possible combination of noise levels in these ranges, and to return the discovered net that scores best on a combined metric. This combined metric includes a fitness metric, a precision metric, and a simplicity metric. As such, the Xcavate plug-in can handle event logs at different noise levels and return a best net.
• Precision: A (relative) weight for the precision metric4. • Simplicity: A (relative) weight for the simplicity metric. • Select classifier: The classifier to use. • Number of threads to use for replay: The number of threads that can be used by the replayer (required to determine fitness and/or precision). • Maximal number of transitions: The maximal number of transitions a Petri net may contain to do a replay. • Prefer WF net: Whether WF nets are preferred over non-WF nets.
The first two parameters determine the ‘excavation site'. For every possible combination of these two thresholds, the plug-in will filter the event log using the Log Skeleton filter plug-in and then start the DisCover plug-in on the filtered log using the provided relative thresholds. Provided that the discovered Petri net does not contain too many transitions, it will then be scored using the three weights. In the end, the Xcavate plug-in returns a Petri net discovered at the excavation site that scores best. If WF nets are preferred, it will return a WF net that scores best.
As a first example, we run the Xcavate plug-in on the BPIC 2012 event log [
As a second example, we ran the Xcavate plug-in on the PDC 2022 [
From https://www.promtools.org/ you can download the latest releases for ProM. The latest release is ProM 6.13. To use the Xcavate plug-in, you need to have the DiSCover package (version 6.13.105 or better) installed in ProM.
You can download a screencast on how to excavate Petri nets from event logs from the Latest downloads section on https://hverbeek.win.tue.nl/. In this screencast, we load two event logs, start the Xcavate plug-in, configure it, run it, and show the best nets discovered.
On https://svn.win.tue.nl/repos/prom/Packages/DiSCover/Trunk you will find the sources for the Xcavate and DiSCover plug-ins. ProM and the plug-ins are Open Source.
The Discover plug-in as introduced during the ICPM 2022 conference has been improved in two ways. First, the discovered nets are now relaxed sound. This is the result of first filtering the log and second constructing the components from the corresponding directly-follows matrix. As a result, all components are discovered from the same set of traces, which results in a relaxed sound net. Second, the discovered nets are now possibly simpler. This is the result of selecting a minimal set of components that covers all activities. Potentially, this could lead to a loss of precision, as a relation between two activities may now be lost as they may not end up in a single component. However, given the fact that the discovered models were often too complex, and supported by the fact that the score for the PDC 2022 contest is still comparable, we feel that this possible loss of precision is acceptable.
The Xcavate plug-in has been built on top of the improved DiSCover plug-in and extends it with two options. First, it puts the Log Skeleton filter plug-in in front of the DiSCover plug-in, which allows you to filter out a different of noise than the DiSCover plug-in can filter out. Second, it allows the user to provide two collections of noise thresholds: One for the Log Skeleton filter plugin and one for the built-in (relative) filter in the DiSCover plug-in. The Xcavate plug-in then discovers a net for every possible pair of thresholds and compares the discovered net on its score. This score is the weighted sum of (1) a fitness metric, (2) a precision metric and (3) a simplicity metric. For the fitness metric and the precision metric, the input log will be replayed on the discovered model. The discovered net that results in the best score is then returned by the Xcavate plug-in. By setting different weights for the metrics, the user can influence the result.
For future work, we aim to speed up (where possible) the construction of the components. For this construction a step is now required to compute the set of maximal sets of activities that do not directly follow each other. In a later step, this set is reduced to a minimal set of sets that cover all activities. The first step has a bad complexity and may lead to problems. Although for many event logs the running times for this step are perfectly acceptable, for some other event logs this step takes ages. Possibly, by using the fact that we only need a minimal set later, we could simplify the first step and hence alleviate this problem.