Improving Declarative Process Mining with a Priori Noise Filtering

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

In this paper, we report the results of an exploratory study into the efficacy of noise filtering in improving the accuracy of declarative process mining. We apply the double-granularity mixed-dependency filtering algorithm as introduced by [9], to the DisCoveR declarative miner [1], and parameter optimise it to only perform coarse-grained filtering. However, while noise filtering appears promising on the surface, one might worry that the outlier behaviour allowed by declarative models may be wrongly classified as noise and removed. To test the efficacy of noise filtering from both perspectives, we applied DisCoveR with noise filtering to two data sets: the process log collection used in the PDC2020 process discovery contest, emulating “real-world” scenarios; and a synthetic set of logs known to exhibit (non-noise) outlier behaviour. We find that on the contest data sets, noise filtering significantly increases accuracy (on average 23% points), obtaining exploratory evidence that noise filtering may improve declarative miner performance; on the synthetic logs we showcase examples where noise is filtered, while outlier behaviour remains untouched.

OriginalsprogEngelsk
TitelBusiness Process Management Workshops - BPM 2022 International Workshops, Revised Selected Papers
RedaktørerCristina Cabanillas, Niels Frederik Garmann-Johnsen, Agnes Koschmider
ForlagSpringer
Publikationsdato2023
Sider286-297
ISBN (Trykt)9783031253829
DOI
StatusUdgivet - 2023
BegivenhedWorkshops on AI4BPM, BP-Meet-IoT, BPI, BPM and RD, BPMS2, BPO, DEC2H, and NLP4BPM 2022, co-located with the 20th International Conference on Business Process Management, BPM 2022 - Münster, Tyskland
Varighed: 11 sep. 202216 sep. 2022

Konference

KonferenceWorkshops on AI4BPM, BP-Meet-IoT, BPI, BPM and RD, BPMS2, BPO, DEC2H, and NLP4BPM 2022, co-located with the 20th International Conference on Business Process Management, BPM 2022
LandTyskland
ByMünster
Periode11/09/202216/09/2022
NavnLecture Notes in Business Information Processing
Vol/bind460 LNBIP
ISSN1865-1348

Bibliografisk note

Funding Information:
Work supported by the Innovation Fund Denmark project EcoKnow (7050-00034A), Digital Research Centre Denmark and DCR Solutions A/S.

Publisher Copyright:
© 2023, Springer Nature Switzerland AG.

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