Szczegóły publikacji
Opis bibliograficzny
Brief overview of selected research directions and applications of process mining in KRaKEn Research Group / Krzysztof KLUZA, Mateusz ZAREMBA, Dominik SEPIOŁO, Piotr WIŚNIEWSKI, Weronika T. ADRIAN, Maria Teresa Gaudio, Paweł JEMIOŁO, Marek ADRIAN, Krystian JOBCZYK, Mateusz ŚLAŻYŃSKI, Bernadetta STACHURA-TERLECKA, Antoni LIGĘZA // W: Progress in Polish artificial intelligence research 4 [Dokument elektroniczny] / ed. by Adam Wojciechowski, Piotr Lipiński. — Wersja do Windows. — Dane tekstowe. — Łódź : Łódź University of Technology Press, 2023. — (Monografie Politechniki Łódzkiej ; nr 2437). — e-ISBN: 978-83-66741-92-8. — S. 151–156. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: http://repozytorium.p.lodz.pl/bitstream/handle/11652/4773/Pro... [2023-10-02]. — Bibliogr. s. 155–156, Abstr.
Autorzy (12)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 149071 |
|---|---|
| Data dodania do BaDAP | 2023-10-23 |
| DOI | 10.34658/9788366741928.22 |
| Rok publikacji | 2023 |
| Typ publikacji | fragment książki |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Politechnika Łódzka |
| Czasopismo/seria | Monografie Politechniki Łódzkiej |
Abstract
Process mining allows for exploring processes using data from event logs. By providing insights into how processes are actually executed, rather than how they are supposed to be executed, process mining can be used for optimizing business processes and improving organizational efficiency. In this exploratory paper, we report on selected research threads related to process mining carried out within KRaKEn Research Group at AGH University of Science and Technology. We introduce a collection of initial ideas that require further exploration. Our research threads are concerned with the use of process mining techniques 1) for discovering processes from unstructured data, specifically text from e-mails, 2) for explaining black-box machine learning models, using process models as a global explanation, and 3) for analyzing data from different food industry systems to identify inefficiencies and provide recommendations for improvement.