Visualizes an event log as a log skeleton. Input An event log Configuration An event classifier Defaults to the first classifier found in the input log, or the MXML Legacy Classifier if the input log contains no classifiers. Output A JComponent that visualizes the log as a log skeleton (using the Log Skeleton Browser and … [Read more…]
Filters a provided log using different log skeletons created for the provided reference log. Traces that violate the different log skeletons are filtered out. First, the log skeleton for the reference log is used. Second, a collection of filters are used on the reference log, which result in additional log skeletons. The log itself will … [Read more…]
Creates a log from the provided log containing exactly those traces from the provided log that do not violate the provided log skeleton. Input A log skeleton An event log Configuration An event classifier Defaults to the first classifier found in the input log, or the MXML Legacy Classifier if the input log contains no … [Read more…]
Creates a log skeleton from an event log. Input An event log. Configuration An event classifier. Defaults to the first classifier found in the input log, or the MXML Legacy Classifier if the input log contains no classifiers. Panel Output A log skeleton. Variants Build Log Skeleton from Event Log using User Configuration Requires only … [Read more…]
Visualizes a log skeleton. Input A log skeleton Configuration A set of activities to show. Defaults to all activities. A list of relations to show. Defaults to all relations. An option whether to use combine arcs into hyper arcs where possible. Defaults to false, as this combining may take considerable time. An option whether to … [Read more…]
Yesterday, I received the “Process Discovery Contest Award” for my winning submission to the Process Discovery Contest 2019. Many thanks to Massimiliano, Benoit, and Josep for organizing the contest. Next year, I will help them :-).
The nets I discovered for the Process Discovery Contest (PDC) 2019), classified 898 out of 900 correctly as fitting or non-fitting. Hence, I achieved with them a classification accuracy of 99.78%. Below, you’ll find all discovered nets. All nets are sound WF-nets. Both nets 9 and 10 classify a single trace wrongly. All nets were … [Read more…]
This screencast shows how the Log Skeleton Filter an Browser is used to ‘discover’ a sound WF net from training log 4 of the Process Discovery Challenge 2019.
The Log Skeleton Filter and Browser demo has been accepted for the ICPM 2019 conference.
Version 6.9.91 of the LogSkeleton package includes a new log visualizer plug-in that allows the user to use any classifier for the log skeletons. So far, the log skeletons were restricted to the “concept:name” attribute, but this restriction has now been lifted. The screenshot below shows the result of using the “org:resource” classifier on the … [Read more…]
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