Szczegóły publikacji
Opis bibliograficzny
Creation of an event log from a low-level machinery monitoring system for process mining purposes / Edyta BRZYCHCZY, Agnieszka TRZCIONKOWSKA // W: Intelligent Data Engineering and Automated Learning – IDEAL 2018 : 19th international conference : Madrid, Spain, November 21–23, 2018 : proceedings, Pt. 2 / eds. Hujun Yin, [et al.]. — Cham: Springer Nature Switzerland AG, cop. 2018. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; 11315). — ISBN: 978-3-030-03495-5; e-ISBN: 978-3-030-03496-2. — S. 54–63. — Bibliogr. s. 62–63, Abstr.
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 118620 |
|---|---|
| Data dodania do BaDAP | 2019-01-03 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-030-03496-2_7 |
| Rok publikacji | 2018 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Intelligent Data Engineering and Automated Learning 2018 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Industrial event logs, especially from low-level monitoring systems, very often have no suitable structure for process-oriented analysis techniques (i.e. process mining). Such a structure should contain three main elements for process analysis, namely: timestamp of activity, activity name and case id. In this paper we present example data from a low-level machinery monitoring system used in underground mine, which can be used for the modelling and analysis of the mining process carried out in a longwall face. Raw data from the mentioned machinery monitoring system needs significant pre-processing due to the creation of a suitable event log for process mining purposes, because case id and activities are not given directly in the data. In our previous works we presented a mixture of supervised and unsupervised data mining techniques as well as domain knowledge as methods for the activity/process stages discovery in the raw data. In this paper we focus on case id identification with an heuristic approach. We summarize our experiences in this area showing the problems of real industrial data sets.
