TY  - CHAP
A1  - Grum, Marcus
A1  - Kotarski, David
A1  - Ambros, Maximilian
A1  - Biru, Tibebu
A1  - Krallmann, Hermann
A1  - Gronau, Norbert
ED  - Shishkov, Boris
T1  - Managing knowledge of intelligent systems
BT  - the design of a chatbot using domain-specific knowledge
T2  - Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5–7, 2021, Proceedings
N2  - Since more and more business tasks are enabled by Artificial Intelligence (AI)-based techniques, the number of knowledge-intensive tasks increase as trivial tasks can be automated and non-trivial tasks demand human-machine interactions. With this, challenges regarding the management of knowledge workers and machines rise [9]. Furthermore, knowledge workers experience time pressure, which can lead to a decrease in output quality. Artificial Intelligence-based systems (AIS) have the potential to assist human workers in knowledge-intensive work. By providing a domain-specific language, contextual and situational awareness as well as their process embedding can be specified, which enables the management of human and AIS to ease knowledge transfer in a way that process time, cost and quality are improved significantly. This contribution outlines a framework to designing these systems and accounts for their implementation.
KW  - domain-specific language
KW  - morphologic box
KW  - explainability
Y1  - 2021
SN  - 978-3-030-79975-5
SN  - 978-3-030-79976-2
U6  - https://doi.org/10.1007/978-3-030-79976-2_5
VL  - 422
SP  - 78
EP  - 96
PB  - Springer International Publishing
CY  - Cham
ER  - 
TY  - CHAP
A1  - Rojahn, Marcel
A1  - Ambros, Maximilian
A1  - Biru, Tibebu
A1  - Krallmann, Hermann
A1  - Gronau, Norbert
A1  - Grum, Marcus
ED  - Rutkowski, Leszek
ED  - Scherer, Rafał
ED  - Korytkowski, Marcin
ED  - Pedrycz, Witold
ED  - Tadeusiewicz, Ryszard
ED  - 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
SN  - 978-3-031-42504-2
SN  - 978-3-031-42505-9
U6  - https://doi.org/10.1007/978-3-031-42505-9_48
SP  - 570
EP  - 588
PB  - Springer
CY  - Cham
ER  -