TY - JOUR A1 - Haarmann, Stephan A1 - Holfter, Adrian A1 - Pufahl, Luise A1 - Weske, Mathias T1 - Formal framework for checking compliance of data-driven case management JF - Journal on data semantics : JoDS N2 - Business processes are often specified in descriptive or normative models. Both types of models should adhere to internal and external regulations, such as company guidelines or laws. Employing compliance checking techniques, it is possible to verify process models against rules. While traditionally compliance checking focuses on well-structured processes, we address case management scenarios. In case management, knowledge workers drive multi-variant and adaptive processes. Our contribution is based on the fragment-based case management approach, which splits a process into a set of fragments. The fragments are synchronized through shared data but can, otherwise, be dynamically instantiated and executed. We formalize case models using Petri nets. We demonstrate the formalization for design-time and run-time compliance checking and present a proof-of-concept implementation. The application of the implemented compliance checking approach to a use case exemplifies its effectiveness while designing a case model. The empirical evaluation on a set of case models for measuring the performance of the approach shows that rules can often be checked in less than a second. KW - Compliance checking KW - Case management KW - Model verification KW - Data-centric KW - processes Y1 - 2021 U6 - https://doi.org/10.1007/s13740-021-00120-3 SN - 1861-2032 SN - 1861-2040 VL - 10 IS - 1-2 SP - 143 EP - 163 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Ihde, Sven A1 - Pufahl, Luise A1 - Völker, Maximilian A1 - Goel, Asvin A1 - Weske, Mathias T1 - A framework for modeling and executing task BT - specific resource allocations in business processes JF - Computing : archives for informatics and numerical computation N2 - As resources are valuable assets, organizations have to decide which resources to allocate to business process tasks in a way that the process is executed not only effectively but also efficiently. Traditional role-based resource allocation leads to effective process executions, since each task is performed by a resource that has the required skills and competencies to do so. However, the resulting allocations are typically not as efficient as they could be, since optimization techniques have yet to find their way in traditional business process management scenarios. On the other hand, operations research provides a rich set of analytical methods for supporting problem-specific decisions on resource allocation. This paper provides a novel framework for creating transparency on existing tasks and resources, supporting individualized allocations for each activity in a process, and the possibility to integrate problem-specific analytical methods of the operations research domain. To validate the framework, the paper reports on the design and prototypical implementation of a software architecture, which extends a traditional process engine with a dedicated resource management component. This component allows us to define specific resource allocation problems at design time, and it also facilitates optimized resource allocation at run time. The framework is evaluated using a real-world parcel delivery process. The evaluation shows that the quality of the allocation results increase significantly with a technique from operations research in contrast to the traditional applied rule-based approach. KW - Process Execution KW - Business Process Management KW - Resource Allocation KW - Resource Management KW - Activity-oriented Optimization Y1 - 2022 U6 - https://doi.org/10.1007/s00607-022-01093-2 SN - 0010-485X SN - 1436-5057 VL - 104 SP - 2405 EP - 2429 PB - Springer CY - Wien ER - 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 - 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 -