Bootstrapping Generalization of Discovered Processes
Channel: Artem Polyvyanyy
Date: 08/08/2021

Presenter: Artem Polyvyanyy (http://polyvyanyy.com/)

Preprint: https://arxiv.org/abs/2107.03876

Abstract: Process mining studies ways to derive value from process executions recorded in event logs of IT-systems, with process discovery the task of inferring a process model for an event log emitted by some unknown system. One quality criterion for discovered process models is generalization. Generalization seeks to quantify how well the discovered model describes future executions of the system, and is perhaps the least understood quality criterion in process mining. The lack of understanding is primarily a consequence of generalization seeking to measure properties over the entire future behavior of the system, when the only available sample of behavior is that provided by the event log itself. In this seminar, I introduce you to the research discipline of process mining, discuss the generalization quality criterion for discovered processes, and share our recent ideas on tackling the problem of measuring generalization using the bootstrap method known from computational statistics.

#processmining
#conformancechecking
#generalization
#bootstrap
#computationalstatistics
#statistics
#inference
#datascience
#processscience