biostudies-literatureUnknown12127Dustdar SComparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly-follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences.Advanced Information Systems Engineering32nd International Conference, CAiSE 2020, Grenoble, France, June 8–12, 2020, Proceedings299-318https://www.ebi.ac.uk/biostudies/studies/S-EPMC7266464biostudies-literatureBusiness Process Variant Analysis Based on Mutual Fingerprints of Event LogsPMC7266464Dustdar SCarmona JPant VTaymouri FLa Rosa MYu ESalinesi CRieu DfalseBusiness Process Variant Analysis Based on Mutual Fingerprints of Event LogsComparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly-follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences.2020-01-01T00:00:00Z2020 May2020-09-28T07:00:48Z2020-09-28T07:00:48ZS-EPMC7266464