TY  - GEN
A1  - Gonzalez-Lopez, Fernanda
A1  - Pufahl, Luise
T1  - A Landscape for Case Models
T2  - Enterprise, Business-Process and Information Systems Modeling
N2  - Case Management is a paradigm to support knowledge-intensive processes. The different approaches developed for modeling these types of processes tend to result in scattered models due to the low abstraction level at which the inherently complex processes are therein represented. Thus, readability and understandability is more challenging than that of traditional process models. By reviewing existing proposals in the field of process overviews and case models, this paper extends a case modeling language - the fragment-based Case Management (fCM) language - with the goal of modeling knowledge-intensive processes from a higher abstraction level - to generate a so-called fCM landscape. This proposal is empirically evaluated via an online experiment. Results indicate that interpreting an fCM landscape might be more effective and efficient than interpreting an informationally equivalent case model.
KW  - Case Management
KW  - Process landscape
KW  - Process map
KW  - Process architecture
KW  - Process model
Y1  - 2019
SN  - 978-3-030-20618-5
SN  - 978-3-030-20617-8
U6  - https://doi.org/10.1007/978-3-030-20618-5_6
SN  - 1865-1348
VL  - 352
SP  - 87
EP  - 102
PB  - Springer
CY  - Berlin
ER  - 
TY  - JOUR
A1  - Leopold, Henrik
A1  - van der Aa, Han
A1  - Offenberg, Jelmer
A1  - Reijers, Hajo A.
T1  - Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels
JF  - Information systems
N2  - Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects.
KW  - Label analysis
KW  - Process model
KW  - Natural language
KW  - Hidden Markov models
Y1  - 2019
U6  - https://doi.org/10.1016/j.is.2019.02.005
SN  - 0306-4379
SN  - 1873-6076
VL  - 83
SP  - 30
EP  - 39
PB  - Elsevier
CY  - Oxford
ER  -