Cloud Process Execution Engine: Architecture and Interfaces

Process Execution Engines are a vital part of Business Process Management (BPM) and Manufacturing Orchestration Management (MOM), as they allow the business or manufacturing logic (expressed in a graphical notation such as BPMN) to be executed. This execution drives and supervises all interactions between humans, machines, software, and the environment. If done right, this will lead to a highly flexible, low-code, and easy to maintain solution, that allows for ad-hoc changes and functional evolution, as well as delivering a wealth of data for data-science applications. The Cloud Process Execution Engine CPEE.org implements a radically distributed scale-out architecture, together with a minimal set of interfaces, to allow for the simplest possible integration with existing services, machines, and existing data-analysis tools. Its open-source components can serve as a blueprint for future development of commercial solutions, and serves as a proven testbed for academic research, teaching, and industrial application since 2008. In this paper we present the architecture, interfaces that make CPEE.org possible, as well as discuss different lifecycle models utilized during execution to provide overarching support for a wide range of data-analysis tasks.Comment: 30 pages, 12 figures, 2 illustration

REMUS

Heutige Unternehmen verwenden mehr und mehr inter-organisationale/externe Services innerhalb ihrer internen Gesch ̈ftsprozesse. Die Vorteile dieses Service-Outsourcings sind vielfältig, und reichen von geringeren Wartungsaufwand bis zu Kostenvorhersage. Jedoch müssen Unternehmen, um sich nicht an einen einzelnen Anbieter zu binden, dafür sorgen, dass die verwendeten Services austauschbar sind. Um diese gewünschte Flexibilität bei der Serviceauswahl zu unterstützen ist ein Markplatz für Services, welcher auf einer gemeinsamen Menge von Regel aufbaut, nötig. In dieser Arbeit stellen wir ein hybrides Prozess und Service Verzeichnis vor, welches als Basis für solch einen Marktplatz verwendet werden kann. Die Einteilung in unterschiedliche An- wendungsdomänen mit gemeinsamen Schnittstellen unterstützt Kunden bei der Nutzung der angebotenen Services. Die Möglichkeit innerhalb der Servicebeschreibungen die Schnittstellen/Parameter zu transformieren erlaubt es Anbietern flexibel mit ihren Services umzuge- hen. Weiter stellen wir in dieser Arbeit einen Prototyp vor, welcher demonstriert, wie diese Information in Kombination mit adaptiven Workflowsystemen innerhalb von Cloud - Infrastrukturen verwendet werden kann. Um die Umsetzbarkeit der vorgestellten Konzepte zu zeigen wird eine Anwendung für mobile Geräte vorgestellt, welche ein reales Beispiel unter Einbeziehung des Marktplatzes ausführt.Today’s companies more and more embrace the utilization of inter-organizational services as part of their internal business processes. The benefit from this outsourcing is manifold, ranging from lower maintenance burden to predictable cost. One problem is, that for companies in order to not bind themselves to a single service provider, they have to ensure that the services they consume are interchangeable. Therefore a marketplace for services, based in a common set of rules, that allows companies to stay flexible when selecting business partners is needed. In this thesis we introduce a hybrid process and service repository acting as a base for such a marketplace. Organizing services into different application domains with a common interface allows easy usage of the provided services for the customers, while the support of interface transformation within service description keeps the vendors flexible. We further introduce a prototype system demonstrating how to use this information on in combination with adaptive workflow execution engine and Cloud infrastructures as a base. To proof the feasibility of the introduced concepts, a mobile client executing a real-world example is introduced

IUPC: Identification and Unification of Process Constraints

Business Process Compliance (BPC) has gained significant momentum in research and practice during the last years. Although many approaches address BPC, they mostly assume the existence of some kind of unified base of process constraints and focus on their verification over the business processes. However, it remains unclear how such an inte- grated process constraint base can be built up, even though this con- stitutes the essential prerequisite for all further compliance checks. In addition, the heterogeneity of process constraints has been neglected so far. Without identification and separation of process constraints from domain rules as well as unification of process constraints, the success- ful IT support of BPC will not be possible. In this technical report we introduce a unified representation framework that enables the identifica- tion of process constraints from domain rules and their later unification within a process constraint base. Separating process constraints from domain rules can lead to significant reduction of compliance checking effort. Unification enables consistency checks and optimizations as well as maintenance and evolution of the constraint base on the other side.Comment: 13 pages, 4 figures, technical repor

10151 Abstracts Collection -- Enabling Holistic Approaches to Business Process Lifecycle Management

From 11.04. to 16.04.2010, the Dagstuhl Seminar 10151 ``Enabling Holistic Approaches to Business Process Lifecycle Management \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML

Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identification and integration of various data sources, the required definition of semantic connections between data attributes, and the definition of data processing steps within the machine learning support. Results: By consolidating the knowledge of domain and machine learning experts, a powerful tool to describe machine learning tasks by formalizing knowledge using the systems modeling language SysML is introduced. The method is evaluated based on two use cases, i.e., a smart weather system that allows to predict weather forecasts based on sensor data, and a waste prevention case for 3D printer filament that cancels the printing if the intended result cannot be achieved (image processing). Further, a user study is conducted to gather insights of potential users regarding perceived workload and usability of the elaborated method. Conclusion: Integrating machine learning-specific properties in systems engineering techniques allows non-data scientists to understand formalized knowledge and define specific aspects of a machine learning problem, document knowledge on the data, and to further support data scientists to use the formalized knowledge as input for an implementation using (semi-) automatic code generation. In this respect, this work contributes by consolidating knowledge from various domains and therefore, fosters the integration of machine learning in industry by involving several stakeholders.Comment: 43 pages, 24 figure, 3 table

Robust Digital Twin Compositions for Industry 4.0 Smart Manufacturing Systems

Industry 4.0 is an emerging business paradigm that is reaping the benefits of enabling technologies driving intelligent systems and environments. By acquiring, processing and acting upon various kinds of relevant context information, smart automated manufacturing systems can make well-informed decisions to adapt and optimize their production processes at runtime. To manage this complexity, the manufacturing world is proposing the ‘Digital Twin’ model to represent physical products in the real space and their virtual counterparts in the virtual space, with data connections to tie the virtual and real products together for an augmented view of the manufacturing workflow. The benefits of such representations are simplified process simulations and efficiency optimizations, predictions, early warnings, etc. However, the robustness and fidelity of digital twins are a critical concern, especially when independently developed production systems and corresponding digital twins interfere with one another in a manufacturing workflow and jeopardize the proper behavior of production systems. We therefore evaluate the addition of safeguards to digital twins for smart cyber-physical production systems (CPPS) in an Industry 4.0 manufacturing workflow in the form of feature toggles that are managed at runtime by software circuit breakers. Our evaluation shows how these improvements can increase the robustness of interacting digital twins by avoiding local errors from cascading through the distributed production or manufacturing workflow.status: publishe