Szczegóły publikacji

Opis bibliograficzny

Generating synthetic datasets for process mining / Jakub SAWCZUK, Marcin SZPYRKA // W: ENASE 2026 [Dokument elektroniczny] : proceedings of the 21st international conference on Evaluation of Novel Approaches to Software Engineering : Benidorm - Spain, May 22 - 24, 2026 , Vol. 2 / eds. Mike Mannion, Tomi Mannisto, Leszek Maciaszek. — Wersja do Windows. — Dane tekstowe. — Portugal : SciTePress – Science and Technology Publications, cop. 2026. — ( ENASE ; ISSN  2184-4895 ). — e-ISBN: 978-989-758-828-0. — S. 1174–1181. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.scitepress.org/PublicationsDetail.aspx?ID=7rC0D1o... [2026-05-22]. — Bibliogr. s. 1181, Abstr. — Dostęp po zalogowaniu

Autorzy (2)

Słowa kluczowe

synthetic logsdiffusion networkGenerative Adversarial NetworkWF-netsprocess mining

Dane bibliometryczne

ID BaDAP167858
Data dodania do BaDAP2026-06-29
Rok publikacji2026
Typ publikacjimateriały konferencyjne (aut.)
Otwarty dostęptak
Creative Commons
KonferencjaInternational Conference on Evaluation of Novel Approaches to Software Engineering 2026
Czasopismo/seriaENASE

Abstract

Testing new ideas related to process mining requires access to datasets on which we can repeatedly test new methods/algorithms. This often requires performing computational experiments on similar datasets (logs) to assess, for example, whether the algorithm is resistant to minor disturbances. Unfortunately, there are few real logs available to the general public, and if we were to search for a set of similar logs, it would be difficult to expect success. This article presents several methods for modifying logs so that their original character is preserved, but some noise is introduced. By using a set of hyper-parameters, the user can control the scale of individual changes that will be introduced and generate as mamy similar logs as necessary. The article presents a method of introducing noise that is completely controlled by the user, as well as methods of generating new traces using GANs and diffusion networks.

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