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Process mining is the research area that is concerned with knowledge discovery from event logs. Process mining faces notable difficulties. One is that process mining is commonly limited to the harder setting of unsupervised learning, since negative information about state transitions that were prevented from taking place (i.e. negative events) is often unavailable in real-life event logs. We propose a method to enhance process event logs with artificially generated negative events, striving towards the induction of a set of negative examples that is both correct (containing no false negative events) and complete (containing all, non-trivial negative events). Such generated sets of negative events can advantageously be applied for discovery and evaluation purposes, and in auditing and compliance settings.

Improved Artificial Negative Event Generation to Enhance Process Event Logs