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
Dane bibliometryczne
| ID BaDAP | 167858 |
|---|---|
| Data dodania do BaDAP | 2026-06-29 |
| DOI | 10.5220/0015039600004015 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Konferencja | International Conference on Evaluation of Novel Approaches to Software Engineering 2026 |
| Czasopismo/seria | ENASE |
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.
