Foundations and practice of binary process discovery
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Foundations and practice of binary process discovery. / Slaats, Tijs; Debois, Søren; Back, Christoffer Olling; Christfort, Axel Kjeld Fjelrad.
In: Information Systems, Vol. 121, 102339, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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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 -
ID: 382758437