TY  - CHAP
A1  - Rojahn, Marcel
A1  - Ambros, Maximilian
A1  - Biru, Tibebu
A1  - Krallmann, Hermann
A1  - Gronau, Norbert
A1  - Grum, Marcus
A2  - Rutkowski, Leszek
A2  - Scherer, Rafał
A2  - Korytkowski, Marcin
A2  - Pedrycz, Witold
A2  - Tadeusiewicz, Ryszard
A2  - Zurada, Jacek M.
T1  - Adequate basis for the data-driven and machine-learning-based identification
T2  - Artificial intelligence and soft computing
N2  - Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.
KW  - data mining
KW  - knowledge engineering
KW  - various applications
Y1  - 2023
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/60766
SN  - 978-3-031-42504-2
SN  - 978-3-031-42505-9
SP  - 570
EP  - 588
PB  - Springer
CY  - Cham
ER  -