TY  - GEN
A1  - Combi, Carlo
A1  - Oliboni, Barbara
A1  - Weske, Mathias
A1  - Zerbato, Francesca
ED  - Trujillo, JC Davis
T1  - Conceptual modeling of processes and data
BT  - Connecting different perspectives
T2  - Conceptual Modeling, ER 2018
N2  - Business processes constantly generate, manipulate, and consume data that are managed by organizational databases. Despite being central to process modeling and execution, the link between processes and data is often handled by developers when the process is implemented, thus leaving the connection unexplored during the conceptual design. In this paper, we introduce, formalize, and evaluate a novel conceptual view that bridges the gap between process and data models, and show some kinds of interesting insights that can be derived from this novel proposal.
Y1  - 2018
SN  - 978-3-030-00847-5
SN  - 978-3-030-00846-8
U6  - https://doi.org/10.1007/978-3-030-00847-5_18
SN  - 0302-9743
SN  - 1611-3349
VL  - 11157
SP  - 236
EP  - 250
PB  - Springer
CY  - Cham
ER  - 
TY  - JOUR
A1  - Bazhenova, Ekaterina
A1  - Zerbato, Francesca
A1  - Oliboni, Barbara
A1  - Weske, Mathias
T1  - From BPMN process models to DMN decision models
JF  - Information systems
N2  - The interplay between process and decision models plays a crucial role in business process management, as decisions may be based on running processes and affect process outcomes. Often process models include decisions that are encoded through process control flow structures and data flow elements, thus reducing process model maintainability. The Decision Model and Notation (DMN) was proposed to achieve separation of concerns and to possibly complement the Business Process Model and Notation (BPMN) for designing decisions related to process models. Nevertheless, deriving decision models from process models remains challenging, especially when the same data underlie both process and decision models. In this paper, we explore how and to which extent the data modeled in BPMN processes and used for decision-making may be represented in the corresponding DMN decision models. To this end, we identify a set of patterns that capture possible representations of data in BPMN processes and that can be used to guide the derivation of decision models related to existing process models. Throughout the paper we refer to real-world healthcare processes to show the applicability of the proposed approach. (C) 2019 Elsevier Ltd. All rights reserved.
KW  - Business process models
KW  - Decision models
KW  - BPMN
KW  - DMN
KW  - Pattern
Y1  - 2019
U6  - https://doi.org/10.1016/j.is.2019.02.001
SN  - 0306-4379
SN  - 1873-6076
VL  - 83
SP  - 69
EP  - 88
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Combi, Carlo
A1  - Oliboni, Barbara
A1  - Weske, Mathias
A1  - Zerbato, Francesca
T1  - Seamless conceptual modeling of processes with transactional and analytical data
JF  - Data & knowledge engineering
N2  - In the field of Business Process Management (BPM), modeling business processes and related data is a critical issue since process activities need to manage data stored in databases. The connection between processes and data is usually handled at the implementation level, even if modeling both processes and data at the conceptual level should help designers in improving business process models and identifying requirements for implementation. Especially in data -and decision-intensive contexts, business process activities need to access data stored both in databases and data warehouses. In this paper, we complete our approach for defining a novel conceptual view that bridges process activities and data. The proposed approach allows the designer to model the connection between business processes and database models and define the operations to perform, providing interesting insights on the overall connected perspective and hints for identifying activities that are crucial for decision support.
KW  - Conceptual modeling
KW  - Business process modeling
KW  - BPMN
KW  - Data modeling
KW  - Data warehouse
KW  - Decision support
Y1  - 2021
U6  - https://doi.org/10.1016/j.datak.2021.101895
SN  - 0169-023X
SN  - 1872-6933
VL  - 134
PB  - Elsevier
CY  - Amsterdam
ER  -