TY - JOUR

T1 - Foundations and practice of binary process discovery

AU - Slaats, Tijs

AU - Debois, Søren

AU - Back, Christoffer Olling

AU - Christfort, Axel Kjeld Fjelrad

N1 - Publisher Copyright: © 2023

PY - 2024

Y1 - 2024

N2 - Most contemporary process discovery methods take as inputs only positive examples of process executions, and so they are one-class classification algorithms. However, we have found negative examples to also be available in industry, hence we build on earlier work that treats process discovery as a binary classification problem. This approach opens the door to many well-established methods and metrics from machine learning, in particular to improve the distinction between what should and should not be allowed by the output model. Concretely, we (1) present a verified formalisation of process discovery as a binary classification problem; (2) provide cases with negative examples from industry, including real-life logs; (3) propose the Rejection Miner binary classification procedure, applicable to any process notation that has a suitable syntactic composition operator; (4) implement two concrete binary miners, one outputting Declare patterns, the other Dynamic Condition Response (DCR) graphs; and (5) apply these miners to real world and synthetic logs obtained from our industry partners and the process discovery contest, showing increased output model quality in terms of accuracy and model size.

AB - Most contemporary process discovery methods take as inputs only positive examples of process executions, and so they are one-class classification algorithms. However, we have found negative examples to also be available in industry, hence we build on earlier work that treats process discovery as a binary classification problem. This approach opens the door to many well-established methods and metrics from machine learning, in particular to improve the distinction between what should and should not be allowed by the output model. Concretely, we (1) present a verified formalisation of process discovery as a binary classification problem; (2) provide cases with negative examples from industry, including real-life logs; (3) propose the Rejection Miner binary classification procedure, applicable to any process notation that has a suitable syntactic composition operator; (4) implement two concrete binary miners, one outputting Declare patterns, the other Dynamic Condition Response (DCR) graphs; and (5) apply these miners to real world and synthetic logs obtained from our industry partners and the process discovery contest, showing increased output model quality in terms of accuracy and model size.

KW - Binary classification

KW - DisCoveR

KW - Dynamic condition response graphs

KW - Labelled event logs

KW - Negative examples

KW - Process mining

U2 - 10.1016/j.is.2023.102339

DO - 10.1016/j.is.2023.102339

M3 - Journal article

AN - SCOPUS:85183743346

VL - 121

JO - Information Systems

JF - Information Systems

SN - 0306-4379

M1 - 102339

ER -