TY - JOUR A1 - Meyer, Andreas A1 - Pufahl, Luise A1 - Batoulis, Kimon A1 - Fahland, Dirk A1 - Weske, Mathias T1 - Automating data exchange in process choreographies JF - Information systems N2 - Communication between organizations is formalized as process choreographies in daily business. While the correct ordering of exchanged messages can be modeled and enacted with current choreography techniques, no approach exists to describe and automate the exchange of data between processes in a choreography using messages. This paper describes an entirely model-driven approach for BPMN introducing a few concepts that suffice to model data retrieval, data transformation, message exchange, and correlation four aspects of data exchange. For automation, this work utilizes a recent concept to enact data dependencies in internal processes. We present a modeling guideline to derive local process models from a given choreography; their operational semantics allows to correctly enact the entire choreography from the derived models only including the exchange of data. Targeting on successful interactions, we discuss means to ensure correct process choreography modeling. Finally, we implemented our approach by extending the camunda BPM platform with our approach and show its feasibility by realizing all service interaction patterns using only model-based concepts. (C) 2015 Elsevier Ltd. All rights reserved. KW - Process modeling KW - Data modeling KW - Process choreographies KW - Data exchange KW - BPMN KW - SQL Y1 - 2015 U6 - https://doi.org/10.1016/j.is.2015.03.008 SN - 0306-4379 SN - 1873-6076 VL - 53 SP - 296 EP - 329 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Herzberg, Nico A1 - Meyer, Andreas A1 - Weske, Mathias T1 - Improving business process intelligence by observing object state transitions JF - Data & knowledge engineering N2 - During the execution of business processes several events happen that are recorded in the company's information systems. These events deliver insights into process executions so that process monitoring and analysis can be performed resulting, for instance, in prediction of upcoming process steps or the analysis of the run time of single steps. While event capturing is trivial when a process engine with integrated logging capabilities is used, manual process execution environments do not provide automatic logging of events, so that typically external devices, like bar code scanners, have to be used. As experience shows, these manual steps are error-prone and induce additional work. Therefore, we use object state transitions as additional monitoring information, so-called object state transition events. Based on these object state transition events, we reason about the enablement and termination of activities and provide the basis for process monitoring and analysis in terms of a large event log. In this paper, we present the concept to utilize information from these object state transition events for capturing process progress. Furthermore, we discuss a methodology to create the required design time artifacts that then are used for monitoring at run time. In a proof-of-concept implementation, we show how the design time and run time side work and prove applicability of the introduced concept of object state transition events. (C) 2015 Elsevier B.V. All rights reserved. KW - Business process management KW - Events KW - Data KW - Process Monitoring KW - BPMN Y1 - 2015 U6 - https://doi.org/10.1016/j.datak.2015.07.008 SN - 0169-023X SN - 1872-6933 VL - 98 SP - 144 EP - 164 PB - Elsevier CY - Amsterdam ER - TY - BOOK A1 - Meyer, Andreas A1 - Pufahl, Luise A1 - Fahland, Dirk A1 - Weske, Mathias T1 - Modeling and enacting complex data dependencies in business processes N2 - Enacting business processes in process engines requires the coverage of control flow, resource assignments, and process data. While the first two aspects are well supported in current process engines, data dependencies need to be added and maintained manually by a process engineer. Thus, this task is error-prone and time-consuming. In this report, we address the problem of modeling processes with complex data dependencies, e.g., m:n relationships, and their automatic enactment from process models. First, we extend BPMN data objects with few annotations to allow data dependency handling as well as data instance differentiation. Second, we introduce a pattern-based approach to derive SQL queries from process models utilizing the above mentioned extensions. Therewith, we allow automatic enactment of data-aware BPMN process models. We implemented our approach for the Activiti process engine to show applicability. N2 - Die Ausführung von Geschäftsprozessen in Process Engines benötigt Informationen über den Kontrollfluss, die Rollenzuordnungen und die Datenabhängigkeiten. Während die ersten beiden Aspekte bereits automatisiert von Process Engines unterstützt werden, müssen die Datenabhängigkeiten durch einen Prozessingenieur manuell hinzugefügt und gewartet werden. Allerdings ist diese Aufgabe sehr fehleranfällig und zeitintensiv. In diesem Report zeigen wir wie Prozesse mit komplexen Datenabhängigkeiten, z.B. m:n Beziehungen, modelliert und automatisiert ausgeführt werden können. Dazu erweitern wir zuerst BPMN Datenobjekte mit wenigen Annotationen, um das Handling von Datenabhängikeiten sowie die Differenzierung von Datenobjektinstanzen zu ermöglichen. Danach beschreiben wir einen Pattern-basierten Ansatz, um SQL-Queries, unter Nutzung der oben erwähnten Erweiterungen, aus Prozessmodellen abzuleiten. Damit erlauben wir die automatisierte Ausführung von Daten-orientierten BPMN Prozessmodellen. Um die Anwendbarkeit unseres Ansatzen zu demonstieren, implementierten wir ihn für die Process Engine Activiti. T3 - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 74 KW - Prozessmodellierung KW - Datenmodellierung KW - Prozessausführung KW - BPMN KW - SQL KW - Process Modeling KW - Data Modeling KW - Process Enactment KW - BPMN KW - SQL Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-65103 SN - 978-3-86956-245-2 PB - Universitätsverlag Potsdam CY - Potsdam ER -